{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batch Normalization\n",
    "One way to make deep networks easier to train is to use more sophisticated optimization procedures such as SGD+momentum, RMSProp, or Adam. Another strategy is to change the architecture of the network to make it easier to train. \n",
    "One idea along these lines is batch normalization which was proposed by [3] in 2015.\n",
    "\n",
    "The idea is relatively straightforward. Machine learning methods tend to work better when their input data consists of uncorrelated features with zero mean and unit variance. When training a neural network, we can preprocess the data before feeding it to the network to explicitly decorrelate its features; this will ensure that the first layer of the network sees data that follows a nice distribution. However, even if we preprocess the input data, the activations at deeper layers of the network will likely no longer be decorrelated and will no longer have zero mean or unit variance since they are output from earlier layers in the network. Even worse, during the training process the distribution of features at each layer of the network will shift as the weights of each layer are updated.\n",
    "\n",
    "The authors of [3] hypothesize that the shifting distribution of features inside deep neural networks may make training deep networks more difficult. To overcome this problem, [3] proposes to insert batch normalization layers into the network. At training time, a batch normalization layer uses a minibatch of data to estimate the mean and standard deviation of each feature. These estimated means and standard deviations are then used to center and normalize the features of the minibatch. A running average of these means and standard deviations is kept during training, and at test time these running averages are used to center and normalize features.\n",
    "\n",
    "It is possible that this normalization strategy could reduce the representational power of the network, since it may sometimes be optimal for certain layers to have features that are not zero-mean or unit variance. To this end, the batch normalization layer includes learnable shift and scale parameters for each feature dimension.\n",
    "\n",
    "[3] [Sergey Ioffe and Christian Szegedy, \"Batch Normalization: Accelerating Deep Network Training by Reducing\n",
    "Internal Covariate Shift\", ICML 2015.](https://arxiv.org/abs/1502.03167)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "# As usual, a bit of setup\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.fc_net import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "    \"\"\" returns relative error \"\"\"\n",
    "    return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))\n",
    "\n",
    "def print_mean_std(x,axis=0):\n",
    "    print('  means: ', x.mean(axis=axis))\n",
    "    print('  stds:  ', x.std(axis=axis))\n",
    "    print() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train:  (49000, 3, 32, 32)\n",
      "y_train:  (49000,)\n",
      "X_val:  (1000, 3, 32, 32)\n",
      "y_val:  (1000,)\n",
      "X_test:  (1000, 3, 32, 32)\n",
      "y_test:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in data.items():\n",
    "    print('%s: ' % k, v.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch normalization: forward\n",
    "In the file `cs231n/layers.py`, implement the batch normalization forward pass in the function `batchnorm_forward`. Once you have done so, run the following to test your implementation.\n",
    "\n",
    "Referencing the paper linked to above would be helpful!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before batch normalization:\n",
      "  means:  [ -2.3814598  -13.18038246   1.91780462]\n",
      "  stds:   [27.18502186 34.21455511 37.68611762]\n",
      "\n",
      "After batch normalization (gamma=1, beta=0)\n",
      "  means:  [2.22044605e-17 8.16013923e-17 4.57966998e-17]\n",
      "  stds:   [0.99999999 1.         1.        ]\n",
      "\n",
      "After batch normalization (gamma= [1. 2. 3.] , beta= [11. 12. 13.] )\n",
      "  means:  [11. 12. 13.]\n",
      "  stds:   [0.99999999 1.99999999 2.99999999]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after batch normalization   \n",
    "\n",
    "# Simulate the forward pass for a two-layer network\n",
    "np.random.seed(231)\n",
    "N, D1, D2, D3 = 200, 50, 60, 3\n",
    "X = np.random.randn(N, D1)\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "\n",
    "print('Before batch normalization:')\n",
    "print_mean_std(a,axis=0)\n",
    "\n",
    "gamma = np.ones((D3,))\n",
    "beta = np.zeros((D3,))\n",
    "# Means should be close to zero and stds close to one\n",
    "print('After batch normalization (gamma=1, beta=0)')\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print_mean_std(a_norm,axis=0)\n",
    "\n",
    "gamma = np.asarray([1.0, 2.0, 3.0])\n",
    "beta = np.asarray([11.0, 12.0, 13.0])\n",
    "# Now means should be close to beta and stds close to gamma\n",
    "print('After batch normalization (gamma=', gamma, ', beta=', beta, ')')\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print_mean_std(a_norm,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After batch normalization (test-time):\n",
      "  means:  [-0.0394346  -0.04364982 -0.10494243]\n",
      "  stds:   [1.01947815 1.01606863 0.98208871]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Check the test-time forward pass by running the training-time\n",
    "# forward pass many times to warm up the running averages, and then\n",
    "# checking the means and variances of activations after a test-time\n",
    "# forward pass.\n",
    "\n",
    "np.random.seed(231)\n",
    "N, D1, D2, D3 = 200, 50, 60, 3\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "gamma = np.ones(D3)\n",
    "beta = np.zeros(D3)\n",
    "\n",
    "for t in range(50):\n",
    "    X = np.random.randn(N, D1)\n",
    "    a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "    batchnorm_forward(a, gamma, beta, bn_param)\n",
    "\n",
    "bn_param['mode'] = 'test'\n",
    "X = np.random.randn(N, D1)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, bn_param)\n",
    "\n",
    "# Means should be close to zero and stds close to one, but will be\n",
    "# noisier than training-time forward passes.\n",
    "print('After batch normalization (test-time):')\n",
    "print_mean_std(a_norm,axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch normalization: backward\n",
    "Now implement the backward pass for batch normalization in the function `batchnorm_backward`.\n",
    "\n",
    "To derive the backward pass you should write out the computation graph for batch normalization and backprop through each of the intermediate nodes. Some intermediates may have multiple outgoing branches; make sure to sum gradients across these branches in the backward pass.\n",
    "\n",
    "Once you have finished, run the following to numerically check your backward pass."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx error:  1.6674636109270311e-09\n",
      "dgamma error:  7.378954934067588e-13\n",
      "dbeta error:  2.6853898000928757e-12\n"
     ]
    }
   ],
   "source": [
    "# Gradient check batchnorm backward pass\n",
    "np.random.seed(231)\n",
    "N, D = 4, 5\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "fx = lambda x: batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fg = lambda a: batchnorm_forward(x, a, beta, bn_param)[0]\n",
    "fb = lambda b: batchnorm_forward(x, gamma, b, bn_param)[0]\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma.copy(), dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta.copy(), dout)\n",
    "\n",
    "_, cache = batchnorm_forward(x, gamma, beta, bn_param)\n",
    "dx, dgamma, dbeta = batchnorm_backward(dout, cache)\n",
    "#You should expect to see relative errors between 1e-13 and 1e-8\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dgamma error: ', rel_error(da_num, dgamma))\n",
    "print('dbeta error: ', rel_error(db_num, dbeta))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch normalization: alternative backward\n",
    "In class we talked about two different implementations for the sigmoid backward pass. One strategy is to write out a computation graph composed of simple operations and backprop through all intermediate values. Another strategy is to work out the derivatives on paper. For example, you can derive a very simple formula for the sigmoid function's backward pass by simplifying gradients on paper.\n",
    "\n",
    "Surprisingly, it turns out that you can do a similar simplification for the batch normalization backward pass too.  \n",
    "Given a set of inputs $X=\\begin{bmatrix}x_1\\\\x_2\\\\...\\\\x_N\\end{bmatrix}$, \n",
    "we first calculate the mean $\\mu=\\frac{1}{N}\\sum_{k=1}^N x_k$ and variance $v=\\frac{1}{N}\\sum_{k=1}^N (x_k-\\mu)^2.$    \n",
    "With $\\mu$ and $v$ calculated, we can calculate the standard deviation $\\sigma=\\sqrt{v+\\epsilon}$  and normalized data $Y$ with $y_i=\\frac{x_i-\\mu}{\\sigma}.$\n",
    "\n",
    "\n",
    "The meat of our problem is to get $\\frac{\\partial L}{\\partial X}$ from the upstream gradient $\\frac{\\partial L}{\\partial Y}.$ It might be challenging to directly reason about the gradients over $X$ and $Y$ - try reasoning about it in terms of $x_i$ and $y_i$ first.\n",
    "\n",
    "You will need to come up with the derivations for $\\frac{\\partial L}{\\partial x_i}$, by relying on the Chain Rule to first calculate the intermediate $\\frac{\\partial \\mu}{\\partial x_i}, \\frac{\\partial v}{\\partial x_i}, \\frac{\\partial \\sigma}{\\partial x_i},$ then assemble these pieces to calculate $\\frac{\\partial y_i}{\\partial x_i}$. You should make sure each of the intermediary steps are all as simple as possible. \n",
    "\n",
    "After doing so, implement the simplified batch normalization backward pass in the function `batchnorm_backward_alt` and compare the two implementations by running the following. Your two implementations should compute nearly identical results, but the alternative implementation should be a bit faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx difference:  1.2025174542975708e-12\n",
      "dgamma difference:  0.0\n",
      "dbeta difference:  0.0\n",
      "speedup: 2.00x\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D = 100, 500\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "out, cache = batchnorm_forward(x, gamma, beta, bn_param)\n",
    "\n",
    "t1 = time.time()\n",
    "dx1, dgamma1, dbeta1 = batchnorm_backward(dout, cache)\n",
    "t2 = time.time()\n",
    "dx2, dgamma2, dbeta2 = batchnorm_backward_alt(dout, cache)\n",
    "t3 = time.time()\n",
    "\n",
    "print('dx difference: ', rel_error(dx1, dx2))\n",
    "print('dgamma difference: ', rel_error(dgamma1, dgamma2))\n",
    "print('dbeta difference: ', rel_error(dbeta1, dbeta2))\n",
    "print('speedup: %.2fx' % ((t2 - t1) / (t3 - t2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fully Connected Nets with Batch Normalization\n",
    "Now that you have a working implementation for batch normalization, go back to your `FullyConnectedNet` in the file `cs231n/classifiers/fc_net.py`. Modify your implementation to add batch normalization.\n",
    "\n",
    "Concretely, when the `normalization` flag is set to `\"batchnorm\"` in the constructor, you should insert a batch normalization layer before each ReLU nonlinearity. The outputs from the last layer of the network should not be normalized. Once you are done, run the following to gradient-check your implementation.\n",
    "\n",
    "HINT: You might find it useful to define an additional helper layer similar to those in the file `cs231n/layer_utils.py`. If you decide to do so, do it in the file `cs231n/classifiers/fc_net.py`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "4\n",
      "3\n",
      "2\n",
      "1\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "for i in range(5,-1,-1):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running check with reg =  0\n",
      "Initial loss:  2.2611955101340957\n",
      "W1 relative error: 1.10e-04\n",
      "W2 relative error: 4.03e-06\n",
      "W3 relative error: 4.05e-10\n",
      "b1 relative error: 4.44e-08\n",
      "b2 relative error: 5.55e-09\n",
      "b3 relative error: 1.01e-10\n",
      "beta1 relative error: 7.33e-09\n",
      "beta2 relative error: 1.89e-09\n",
      "gamma1 relative error: 7.47e-09\n",
      "gamma2 relative error: 2.41e-09\n",
      "\n",
      "Running check with reg =  3.14\n",
      "Initial loss:  6.996533220108303\n",
      "W1 relative error: 1.98e-06\n",
      "W2 relative error: 2.28e-06\n",
      "W3 relative error: 1.11e-08\n",
      "b1 relative error: 6.94e-10\n",
      "b2 relative error: 2.78e-09\n",
      "b3 relative error: 2.23e-10\n",
      "beta1 relative error: 6.65e-09\n",
      "beta2 relative error: 3.48e-09\n",
      "gamma1 relative error: 5.94e-09\n",
      "gamma2 relative error: 4.14e-09\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=(N,))\n",
    "\n",
    "# You should expect losses between 1e-4~1e-10 for W, \n",
    "# losses between 1e-08~1e-10 for b,\n",
    "# and losses between 1e-08~1e-09 for beta and gammas.\n",
    "for reg in [0, 3.14]:\n",
    "    print('Running check with reg = ', reg)\n",
    "    model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
    "                            reg=reg, weight_scale=5e-2, dtype=np.float64,\n",
    "                            normalization='batchnorm')\n",
    "\n",
    "    loss, grads = model.loss(X, y)\n",
    "    print('Initial loss: ', loss)\n",
    "\n",
    "    for name in sorted(grads):\n",
    "        f = lambda _: model.loss(X, y)[0]\n",
    "        grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
    "        print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))\n",
    "    if reg == 0: print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batchnorm for deep networks\n",
    "Run the following to train a six-layer network on a subset of 1000 training examples both with and without batch normalization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 200) loss: 2.340974\n",
      "(Epoch 0 / 10) train acc: 0.104000; val_acc: 0.109000\n",
      "(Epoch 1 / 10) train acc: 0.320000; val_acc: 0.269000\n",
      "(Iteration 21 / 200) loss: 2.023269\n",
      "(Epoch 2 / 10) train acc: 0.379000; val_acc: 0.284000\n",
      "(Iteration 41 / 200) loss: 2.062190\n",
      "(Epoch 3 / 10) train acc: 0.470000; val_acc: 0.287000\n",
      "(Iteration 61 / 200) loss: 1.751384\n",
      "(Epoch 4 / 10) train acc: 0.575000; val_acc: 0.309000\n",
      "(Iteration 81 / 200) loss: 1.295173\n",
      "(Epoch 5 / 10) train acc: 0.599000; val_acc: 0.313000\n",
      "(Iteration 101 / 200) loss: 1.287728\n",
      "(Epoch 6 / 10) train acc: 0.714000; val_acc: 0.349000\n",
      "(Iteration 121 / 200) loss: 0.961143\n",
      "(Epoch 7 / 10) train acc: 0.765000; val_acc: 0.349000\n",
      "(Iteration 141 / 200) loss: 1.017802\n",
      "(Epoch 8 / 10) train acc: 0.776000; val_acc: 0.312000\n",
      "(Iteration 161 / 200) loss: 0.751491\n",
      "(Epoch 9 / 10) train acc: 0.824000; val_acc: 0.365000\n",
      "(Iteration 181 / 200) loss: 0.764919\n",
      "(Epoch 10 / 10) train acc: 0.868000; val_acc: 0.331000\n",
      "(Iteration 1 / 200) loss: 2.302332\n",
      "(Epoch 0 / 10) train acc: 0.116000; val_acc: 0.121000\n",
      "(Epoch 1 / 10) train acc: 0.270000; val_acc: 0.234000\n",
      "(Iteration 21 / 200) loss: 2.072398\n",
      "(Epoch 2 / 10) train acc: 0.311000; val_acc: 0.251000\n",
      "(Iteration 41 / 200) loss: 1.852688\n",
      "(Epoch 3 / 10) train acc: 0.374000; val_acc: 0.287000\n",
      "(Iteration 61 / 200) loss: 1.705947\n",
      "(Epoch 4 / 10) train acc: 0.416000; val_acc: 0.309000\n",
      "(Iteration 81 / 200) loss: 1.558138\n",
      "(Epoch 5 / 10) train acc: 0.453000; val_acc: 0.319000\n",
      "(Iteration 101 / 200) loss: 1.652772\n",
      "(Epoch 6 / 10) train acc: 0.488000; val_acc: 0.329000\n",
      "(Iteration 121 / 200) loss: 1.380955\n",
      "(Epoch 7 / 10) train acc: 0.540000; val_acc: 0.322000\n",
      "(Iteration 141 / 200) loss: 1.262761\n",
      "(Epoch 8 / 10) train acc: 0.585000; val_acc: 0.334000\n",
      "(Iteration 161 / 200) loss: 1.078577\n",
      "(Epoch 9 / 10) train acc: 0.631000; val_acc: 0.349000\n",
      "(Iteration 181 / 200) loss: 0.927825\n",
      "(Epoch 10 / 10) train acc: 0.701000; val_acc: 0.338000\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "# Try training a very deep net with batchnorm\n",
    "hidden_dims = [100, 100, 100, 100, 100]\n",
    "\n",
    "num_train = 1000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "weight_scale = 2e-2\n",
    "bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization='batchnorm')\n",
    "model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n",
    "\n",
    "bn_solver = Solver(bn_model, small_data,\n",
    "                num_epochs=10, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True,print_every=20)\n",
    "bn_solver.train()\n",
    "\n",
    "solver = Solver(model, small_data,\n",
    "                num_epochs=10, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=20)\n",
    "solver.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following to visualize the results from two networks trained above. You should find that using batch normalization helps the network to converge much faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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slN06OOPrdpk6Y6DmtKbOmA3sVZMx1SqoK3S8tMqqlVqS6aEJRkYGcFNtBzYPLMCS1Haslg2oz46Vgw5tuh7f3Pwb/NvRc2KdwdNd00hEREREBLApSlWwK780Nki5+ZHni+bYZdI1WHPZHCyd11T0emENXVHZZSoNHHdirgxSUoAaHneskhpa2DXHMBlRgg/84QfYXncDpqbGB5tDKoUURtCrJuN2XIEFf35trIIlt/tPRERERNXHa1MUllxWAbfyTMeh5CgemfCTkQVYm752bMRB5j2AyFgZpFUwB5TWcv6iVblmGC4KA9LtykFrZQQpAaamDmK13IvdW+7VP5cQud1/IiIiIiI7LLmsAm7lmXYdM43bl85rMmSLFgP4Ru6ft83OB3MuShlCbm6OkTkZGDgKDI9lB7OGAem25aAG9TKAawYeGDv/GPBy/4mIiIiIrDCgqxLFAVkxu46ZjQ3W2THjeq+XJ3S7p3ndWs47jSaYu6x47Z1p372nXo/OF06D9GWxoe4q3KLuRu3wMcfTaUwdcjvjSOnefyIiIiKiApZckmtJplFhvVdPXxYKQO/IJMv3HEIKI0rwOqZg55xv2DdEKTRROXIAhc6V+MkN9t0pTd0P5884GTuOuwH7J1yJtuMfRu28K3Pr9SAYEesf72OZU2zuRHno3H8iIiIiIiMGdFS0Rk6Qa5Zi15DDvN5r7dAy9Ku6on2yqg7/78CX8YE//ADnHbsDX9j5fmzs6rE+uNVogsJcOzdWweCvH8xl+Nr6kPrzezBUM6HoW4ZqJqD+YzbvHdS4BU0695+IiIiIyMi1y6WITANwP4BTAIwAuFcpdYdpHwFwB4CPA+gH8BdKqefyr10N4Jb8rt9USv2b20mxy2V8zWzdAvNPzJLUdtxU24GpqUN4HZPxrYFPY/PIgqJ9Ch01x2lrAMa9IwBILgvnxK4LprGjplM5p5F53AKQKxW9dD1noBERERFR5Lx2ufSyhm4IwN8opZ4TkRMBdIrIk0qpFwz7fAzAafk/5wL4VwDnish7ANwKoBm5p/ZOEdmslHpL83ooJqzWe20eWYDO+ouxo3UhzrcI+ACHBh8Tp9oEZR6aqNh1zjRuN6/Bs+OUKWRAR0REREQx5VpyqZR6rZBtU0q9A+C3AMy1YJ8EcL/K+RWABhF5H4BFAJ5USh3OB3FPAvhooFdAkXJb72XXyMO2wYfVaAK3JioFdkFfKR01vQSHUSlT6ScRERERJY/WGjoRmQFgHoD/NL3UBMCYZunOb7PbTgnltt5Lu8HH3GW5ssZ8IxNMnOa9zNFPMAgUB042DVRKCg790G0SQ0RERERVzfPYAhE5AcDDAG5USr1tftniW5TDdqv3Xw5gOQBMnz7d62lRGTiNQLCaeXf7GS9h/s/+FtiUX8d22iXAS08Ur2szr3l7ZLnzmjdg/Jw6t/2NzGvmrAai6wSHQWHpJxERERFpcG2KAgAikgbwGICtSqnvWLx+D4CfKaV+mP96H4ALCn+UUl+y2s8Om6JUkD0dGNp0fdFsuHGRfqH5CBBdYxK7hipSA6gRveAwSH6axBARERFRxQisKUq+g+X3APzWKpjL2wzgOhH5EXJNUY4opV4Tka0AviUiJ+f3uwTAzZ6ugCpC/09Xod406Htc2tY4piCq7JTd2jg1Ut7AyU+TGKKweO0WS0RERJHzUnLZAuDzAJ4Xkd35bV8HMB0AlFJ3A3gcuZEF/43c2IL/mX/tsIj8A4Cd+e9brZQ6HNzpU9xNyL7ubUen5iNhNCYJO3Aq9QH4olXWWcqoSz+rGYOXYuby5MK6TqC67wsREVFMuAZ0SqntsF4LZ9xHAfiKzWvfB/D9ks6OEq93ZBKmpg6671gIpKLKToUZOPl5APazLpD8Y/AyHtd1EhERxZpWl0siXRvqrkK/qivaNm7ZZiGQ8tu1Uoef7ppunB6AvZ7bir250s8Ve/nQHCW/n10litNIDyIiIhrHc5dLIjsbu3qKulquXDRrtNvlmYuXY9WjQ7hR/QiNcgi9ahKeVvNwUU0XTlGH8IZMxs/f99e44/HJ6O3L4uoTvoSbMj9Gffb18LNTXoeO6+IDcHLxsxuP6zqJiIhijQEd+bKxqwc3P/I8soO5tv89fVnc/MjzAIzjDa7FZ7ZehN6+LCZm0nh3YAh/P2hI0/0XAOSyIvcdPQc/Tp9fNNtO93zsgstAOa2z4gNwcvGzG4/rOomIiGKNJZfky7qt+0aDuYLs4DDWbd03+vXSeU3Y0boQ+9sX4/jjajE47DwqIzs4jBt/vBst7duwsavH87kUgsueviwUxoJLr++xsasHLe3bMLN1i/Ox3YZ/R1k6SsHiZzdemOXJRERE5BszdORLb182kO1WzNk+N07Bpdv3u2Uai7g1iWBjk/jx2rmSn521sMqTiYiIyDcGdORLY0MGPRZBWmNDxmJv+/3teA3IAP0g0liemRLBsKlbi+2xvayz4gOwu6jGA+h2ruRnR0RERAnCkkvyZeWiWcika4q2ZdI1WLloluf93RgDMqeySKcg0sxcnmkO5qyOjT0dwG2zAdiUjFbzOitdbmWrVvvfNhtoa8j9bbeflWrpXOnnHhEREVFiMaAjX5bOa8Kay+agqSEDAdDUkHFsaGK1/1XnTUeTTTAGjAVkbmvkdIJLq/JMsyWp7fjlhK/mHpC/PRPY9BXrhhlAuOusKvFBXSfI0g3+zKqhc6Xfe0RERESJxZJL8m2sm6W//c3r2IDigMxtjVzhPW27XBpK/H48MglrU8uweWSB5TkuSW3Ht9MbkMFA/kCH7S9o4rT4lAsmhU6Q5XewdTV0ruTwbyIioqrFgI5iwy0g87JGzja4NAVGU1MH0Z7eAAyiKKirEcGIUvh63b+PBXOOJDf8u5BFy68H23nq9bjxhdP8j0+o1Ad1nSDLS/DntB6vGtruV0MWkoiIiCwxoKNYccr26TZgKWIRGNXLAG6q7cDmgVxAl0nXjJWLtl3p7YQnTrXMos3uvAVnD16DHizw1q3TLiAp5UE9qmYjfugEWW7Bn1sWsxo6V1ZDFpKIiIgscQ0dxZqxCcq7fxhCukaKXndqwFLEJgBqTB2yXvvn5UG4EIBYBIuZfLBYYJ7NV8Rp/ZPdedhtT8paKp3ZZm6z4bysx5u7LJdJbevL/V1JwRzA+XlERERVjBk6ii3zmrq+7CDSKcHJ9Wn09Q/qlTLaZDBSE6dif9vi8ftftApDm65H7fCx0U3DUouaCScB2beKszyPLLc8ZKMcKvradgafTUDS/dDN2FB3FW6pubvoPBwf1JNUoul1PIBbho3lhtWRhSQiIiJLDOgotqyaoAyOKNTX1aJr1SV6b6a5jmrjcAu2D16DG/EjNMoh9KpJuB1XYMEl144PIG2CxV41qehr29JQu+yhHMJ9R8/B0bohrD7+YdRnX3d/UI8yuImytNMp+GO5YQ7n5xEREVUlBnQUW7qDwh1pZjDWbd2HnoEP4yF8uGj7L60GjVsEi1lVh7VDY+89rjTUGAxJClDjRygUAsKHBj6MRwdbMKIUGidksHJ4FpbaXWdUwU2cum9WQ9MTIiIiIhsM6Ci2fDVBsaKRwdAKJi2Cxb2nXo/OF06D2I1PMAYgFsFcvykgLAw+d22wElVwE6fSTr/lhkloIkPVhT+TRESkgQEdxdbKRbMc59Lp2tjVYz+jzsQtmBz/Xi1YumLv6H7zAexYYnMiVsEQAEgNRtQIekcmYe2Q/Yw84+y9cdyCm6AeFG1LOw/kBqBH/RBaarlhnDKNRAB/JomISJuo/G/+46S5uVnt2rWr3KdBMaAThLm9j1VwWNTZ0uP+ALTea5y2BgBW/90JNn7yN+Pe24oA2N9u0czFiflBEchl7+y6Szq5bbZ1aadRqe8dJbvrmDgt1w0zzpjFqUxJ/pkkIqJAiUinUqrZbT+OLaBYWzqvCTtaF2J/+2LsaF1Y2nBuWDdYcRolsHReE9ZcNgdNDZlxYw1032sch1EE5uPWiFjuWlR2Whhq3taQ+9tuRIGX9v5e38uqTb6Z+b2j5PU6vA4t9/JeUUrKeArSx66tRESkiSWXVBVKabBiN+Tcd7MWl3VuxuPaZQpHy051yrPcyiQzJwMDR4HhAff3Mpd2WmYcHY4ZJp174ndoebn4XcPI7F58sWsrERFpYoaOqoJdI5VSGqz4fi+NodpOmUIA3rJuBY4PhArIHh4L5tzeq3AdhWHdE6dZ71OOh1CdexLE0PJy8JPFYXYvfoxZ4IF3gZq64tfZtZWIiBwwQ0dVIcgGK1bvlU4J+geGMLN1i7e1fhpNPOwyhQD0HuytMoNeeAkS4jQ6QOee+B1a7pbpCisT5ieLE6cOpTQ+C5w9DKTSQOY9QPYtZlCJiMiVa0AnIt8H8AkAbyilZlu8vhLAlYb3+yCAKUqpwyLyKoB3AAwDGPKyqI8oDIWAyE+DFWODlomZNCakU+jrH8TETBrvDgzhrf5BAB5GCyC4Zi9aD/ZeyyS9vJfbe4f8EOp4/3SDnVKHlruVY4ZZrukngOYarXixCrBHBoG644Gv7S/PORERUaK4drkUkT8DcBTA/VYBnWnfSwGsUEotzH/9KoBmpdRBnZNil0uKG6eul+u27rMccdDUkMGO1oVa76Ud1PnpXBm3TpUes1mu9y/Ibp5O7/XUauduhGF3Kyw1+8cuivHi0PUWbX1Rnw0REcVIYF0ulVI/B3DY43E/C+CHHvclSgynzpa6TVJ8d8k00liPN47V+rFCqZfue/mlsa7L9f75uSdmTu/llukKOxNmXMO4Yq/363NbN0jRcuh6S0RE5EVga+hEpB7ARwFcZ9isADwhIgrAPUqpe4M6HlGUnII2tyHkOu9VklKHakdcJulIY12Xp/tX6j2xYvdebqWdQXcrDGo9Xpw+d4rX+lMiIkqkIJuiXApgh1LKmM1rUUr1ish7ATwpIi/mM37jiMhyAMsBYPr06QGeFpF/TkGbbsMV3QDQzG39ndb6vCADHzOdAEQjm+X3/gXG7UE8yAf1oNfjhfm5kx4G2ERE5FOQYwuugKncUinVm//7DQCPAjjH7puVUvcqpZqVUs1TpkwJ8LSI/Fu5aBYy6ZqibYWgzXW0gMZ7uSmsH+vpy0JhrAHLxq4eT69HRrc1vkbZmZ/7Fyi30s5SSj/thpjHdXwCBaPU8lkiIiJ4aIoCACIyA8Bjdk1RRGQigP0Apiml3s1vOx5ASin1Tv7fTwJYrZT6D7fjsSkKxVFgnSl9vFdL+zbHBixur0fGQ+MN4z24+oRncYu6G7XDx8b2dWhksnPzPZj23Dq8V72JN2QKDpy1EvOXfCmsqwmN53vwyHKwcQYREVF18doUxcvYgh8CuADAZBHpBnArgDQAKKXuzu/25wCeKARzeX8E4FERKRznQS/BHFFcOc6Di+i93NaPBb4+r1QuJZTmTpX3HT0HR+uGsPr4h1Gffd257GxPB+Y/fyuALCDAKXgTpzx/KzDj5ERlNsz34JqBB1CbOla8UyELF/R6PCIiIqoYrgGdUuqzHva5D8B9pm2vAPhQqSdGVEmCyii5rR+zen1Jaju+XvfvQNuV2utzSs5KugQgVp0qHxr4MH5ZfxF2tLlkEitkMLb5HjSKzXSXI93AZffGt3FGWMPT46ZarpOIiBInyDV0RGRh5+Z7MLvzFpyCN5HKZ5Rmd96CnZvv0X4vt/Vj5teXpLbj2+kNOAVvwtNaNgNf6/FcWuP7yiRWyGBs87X2qsnWO06cGuwohiDprpVMqmq5TiIiSiQGdEQhm/bcOmRkoGhbRgYw7bl12u/l1oDF/PrX6/593LG9NtPwNS/PJQCx60jpqVNlhcztMl/r2qFl6Fd1xTsZs3BxbJxRLc1aquU6iYgokYIcW0BEFt6r3gTEartNiZ0Lt/V3Ra+3XWm9k4dslu/1eA6t8XVHPRRJ8NwuYwnrxEwa6RrB4HCu2cnmkQWoUymsrvewjjAuvGRLK6FUsUKywkREVJkY0BGF7A2Zki95NG+fjFPCPriPZhqNDRmc/faTuKm2A41yEL1qMtYOLUPnSRf7Pq1CwFnS+jyLuV07T70eNz4+Gb0PbvHdgTQs5iYofdlBpFOCk+vT6OsfRGNDBgsWXYv6ef9OwswSAAAgAElEQVRY5jPV4PbzFfT8vHJhUxoiIooxBnREITtw1kpM7LylqPQxq+pw4OyVgQR0jo1LfGSzbj/jJczu3DB63lPlIL6d3oC9Z8wA4H8Egk6nz/HX2IKlhvEHuUApd42FtX6FY8SFVQnr4IhCfV0tulZdUqazKtFo1u0Aculnw0gF489X0A1sosz2GY+VORmoqQOGDeXLCckKExFR5WNARxSy+Uu+hJ1AvsvlQbwhk3Hg7GDmppmzPuOCGYtslteH4Pkv3wlYrP2b//KdAEo4d5eHcbvA1O0andb6xSmgi81ICQ8cf0lgzrpBYTSomzit+HMNslQxymyf+VjZw0AqDWTeA2Tf8h9MVkIZKhERxQYDOqIIzF/yJSAfwJ2S/xMET8GMw1o2R5oP425BwNCm68eGZh85kPs6f35OQZvbNSYlUHIbOREXrr8ksMq6FYK5fNZ0VJClilGOq7A61sggUHc88LX9/t67UspQiYgoNtjlkijBQg1mNLpJuo046P/pqrFgLq92+Bj6f5orWXMK2tyu0UvHzI1dPWhp34aZrVvQ0r7N2+iFgLmNnIgL1+6mOoG+y/gKLVE2JgnzWOyYSUREAWNAR5Rgvtr/u9F4GHcLAiZkX7c8RGG7U9Dmdo1ugZKveXoILhh0GzkRF66/JNAZGxHk/Lwox1WEeSx2zCQiooCx5JIowby0/ze3yhfBaFdFx26QGuvv3IKA3pFJmJoaP6ahd2QSpsK5HNHtGt06ZvpZY+dafqhJpxFMubiWhuo22im15BcoX2OSMEdjsGMmEREFjAEdUYK5BTNWrfILPAUnHh/G3YKADXVX4abBu1BvaLLSr+qwoe4qtME5MPUy4sApUPJTlpqUhiuWSmy84fpLAh+NdrTPP8zGJE7CvMYEz1EkIqJ4YkBHlHBOwYxVQGIUVHDiFgScuXg5Vj06hBvVj9Aoh9CrJuF2XIEFi5ePXkPhfK2CNj+ZLT/NSLSDQbcgKqruhj4ab3iaEegn6+ZVEI1J/NzvsK4xqoCYiIiqBgM6ogrmJQsVRAMVLwEZcC0+s/WikrJsfngpS7WjFQy6BVFRdjf02REyFqWhfteaxbmbZBQBMRERVQ0GdEQVzC4gMe8TBLcgoFxBgqeMkw2tYNAtiLJ7/dEvA48sDzZTUwmNN/yuNYtyzAEREVEZscslUQWz6gBpFMe2+WFYOq8JO1oXYn/7Yuz4+EEs/dkioK0BuG12LpPj8H2eO1O6BVF2r6thAGosg+RwPp5F2REyLH5HHlRCUEtEROQBM3RESaOxLsicnTJ3ubzw9ClYt3UfVvx4t6fMlePw8CQooQzPc2bRLaNk97pRUBkkq8YbqTQw8G4ukE3Cui2/a83YTVJfVGs8iYgoUKKUKvc5jNPc3Kx27dpV7tMgih9zQALkshYlzPYyd8AEchk7uwyU7v7lZBt43jbb5iF/GrBir7c3t3vodftsrF63JEBbn//g2dzyf+Do+Jb/pc6ES4IA/1upCrxfRESxIyKdSqlmt/1YckmUJE7rgjS5DQP3u3+5OA4SD6rRxpEDGFcmOXcZds75Bl7HFIwoweuYgp1zvjH2MGwesi02pbATp/oehj56vBV7gba+XGdIYzAHlPxzkxhBDjWvBgH+bwsREUWLJZdESRLguiDdlvx+5rlFyXF2XIiNNjYOt+Dmne9HdvCO0ZcyO2uwZlrPWGbN2N3QLiNy0Sqse3wfLh7+37iprgONchC9ajLWDi3Duq11pWVDq3U9mbmb5J6OfJY2grERSStdrNafESKiCsAMHVGSBNjswq67ZVDby8Ux8Ayx0YZ2BtMhg9T89pNoT2/A1NRBpASYmjqI9vQGNL/9pLfzNKuEJil+OWVXk3icoPFnxFrhlwAemigREZULAzqiJPEbkBhYdcB06nqpu3+5OAaefsvwHB567QLJnr4sZrZuQUv7tvElk8ayyBV7R8/j5rp/R70Ul0jWywBurvt3b+dpFuDPTazoPGxHVVKY1NLFSv0Z8SOpwTkRVR2WXBIlid/Ofwa689n8zHOLkuvsOB9DnXeeej1md96CjCHYyqo67D31ejT+wX7mn3EdHADXe/ZHOOi6XatpSoA/N0ErufmLbsfSMEsKjSWWsGk0FlXpYqnlnjH+GRknqpJWzjIkooRw7XIpIt8H8AkAbyilZlu8fgGATQD25zc9opRanX/towDuAFADYINSqt3LSbHLJVFMJWRtUFjjFVrat+Hst5/ETbUdaJRD6FWTsHZoGTpPutgykLTS1JDBjtaFzgdy6caZpI6jTnxdh27H0iA6nFrx2r3U73FKPZdK61QZ5TW2NcA6QM91oiUiCpvXLpdeMnT3AfgugPsd9nlGKfUJ0wnUAPgXABcD6AawU0Q2K6Ve8HBMIoqbEma4lYvn2XGaevuy6MECbB5YULRd+rLjMph2vyrz1ETGao6cofzNsfFLggI6X9ehm3Fzuacls8rimEVVulgNGaUor5GzDIkoIVzX0Cmlfg7gcAnvfQ6A/1ZKvaKUGgDwIwCfLOF9iCgOkro2KEBujWGWzmvCjtaF2N++GE1+msi4rPVLSsdRN76uQ7eJR1hjDBxLKSMel6Ab5Cax4UeU3Ti5rpCIEiKoNXTni8ivAfQC+Ful1G8ANAEw/mqrG8C5AR2PiKJWIW3N/ZRjuq7PK3FfSw5r/RobrNfrhdVxNKwSVl/X4SHjNv68W7A06LJH2yxOBCWWns/FIshNUMa9SJRZsyStKySiqhZEl8vnALxfKfUhAHcC2JjfLhb72i7YE5HlIrJLRHa9+eabAZwWEQXK5oGpe2SSZQfHjV09aGnfZt/hsQz8DuxeOq8Jay6bg6aGDAS59XB267109vV67oX7+e4fhpCuKf6f2LA6jgYy5NyGr86pLhm3MM+7SJyyODrnktSMe9T326YTLRFRnLg2RQEAEZkB4DGrpigW+74KoBnAaQDalFKL8ttvBgCl1Bq392BTFKLycMzEWDQj6Fd1aB28BptHFhQ1s4hr046W9m2WGSFPjUrKyOp+plOCEybUoq9/EI0NGVx4+hQ8/eKbo5+d+etSs2ph37MwG9hE9lmXs1mQ+dinXQK89IT7uSSp4Uep10hElHBBNkVxO9ApAH6vlFIicg5yWb9DAPoAnCYiMwH0ALgCwOf8Ho+IwmEOGsa12TeUH40c6UbvSK7D4+aRXIMQYzOLuDbtSOraM6v7OTiiUF9Xi65Vl1h+dg/86nej++qMTDAL+56F2cBGZ7svPkZh+GJVNvnrB72t2UtKww8/11jq8VhiSUQJ41pyKSI/BPBLALNEpFtEvigiXxaRL+d3uRzA3vwauvUArlA5QwCuA7AVwG8BdOTX1hFRDDkFYaPy5UenHvsBFgysHw3mCgoPy3ENnOzWZqVEYlUaauZ2P60+O7Nxn6WRQ3MMt0YwcZXU89bip2wyTqWiTqIsDeUgcSJKKC9dLj+rlHqfUiqtlJqqlPqeUupupdTd+de/q5T6E6XUh5RS5ymlfmH43seVUv+PUupUpdQ/hnkhROSPThDm9rAc14dpqzVbADCsVLjrrHxyu59eA2XL/VweYn2tcyujpJ63Fj+NisLq+hm0KJsxJXVdIRFVvSCaohBRBdAJwtweluP6MG1uVFIj43s3OWayTKJq/OJ2P70Gypb7uTzEBt3cxVVArfQjP+9y0B3dYJaEhh9+r1FHhXTyJaLqE9TYAiJKOJ02++Yh2uZmFm6vl5NxzdbM1i2W+3jJeLmuOQyQ2/20+uzM7D5LdaTbuiXxkQOQtgZg4lQsvWgVlrZG8LAfcCv9sNbnxUZYw9LLwW7tWpTXmJR1hUREJp66XEaNXS6JyiOsjoNxZdcJsUYEI0o53oO4dcw0f3Zeu1y+3vbHOAUuo2LSmWjK8W6bHZ+ZbklRCU08LDroFv3MRXWNbudBRBQxr10uGdARUdWyGgdgZjduYWbrFrum79jfvjjYEw3RV79+M9akN6BeBpx3jCKoirCVfrX98iIUQQVacQrk/VxTJQTXRBQrkY0tICIKWlQP2+ZSxpQIhk2/5LIbt9DYkLHM0JW78YuuXSddjNa3gZtqO9AohyBQsFhaGM06oohK3qIsl61YQZbHhr12TSfQKnUERcDlwkREOtgUhYhipfCw3dOXjaTz5NJ5TdjRuhD72xdjxKZiwWpNXVwbv+hauWgWnqz5CBYMrMcH/vAD9KjJ1jtGsY4oolb6nkZ0VIqAmsyME2RHyDAbn0Q1ioAdMomojBjQEVGslPNhW6fTZ6V0UTRfx4a6qzBUM6F4p6gabUTUSj+ucxIDF2YwY5tVO6AfOIYZyIcdaBUCZqvMMlC+DplhBfJEFEssuSSiWCnnw7ZOp0+gcrooFl/HYmDPn5RvLVCpJW8aKqVc1pVTMOP3HtuVxwL65YaFfcL4mQuznNOqiYqZMctYruYuLP8kqngM6IgoVsr5sB3ncQuRiiCoKifdwL2s/AQBYQYzVuMEjHQDx7B+5sJcl2kVMBsZs4xRBllhBvJEFEsM6IgoVsr9sF0pWbeySUCnv6AD99Ca+FgFARuvBX76NSD7lvv9DTOYKcqqxazc0CjMOXZO1zdxWvFnE2WQxQHpRFWHAR0RxQqzZAmWoFKvoAL3UDtmWgUBI4NA9nDu3273N+yh3IWsmu3YgRgM5A6znNM2YLYYtxBlkMUB6URVhwEdEcUOs2QJVYWlXk5NfHz/DHt52He6v2EGM0ZhB45+hVXOqXPdUQZZcf88iChwDOiIiEiLbYmhTQAycqQbp7ZuQWNDBheePgVPv/jm6PfefsZLmP/ynbEu0XQSahMfp8YjRk6BXxTrIaMKHIMSVFmwznX7DbJ0Z+l5PS8iqgiibOYulVNzc7PatWtXuU+DiChSUQ1U98NcYgjk1jiuuWwOlv5skWUA0j0yGQsG1o/bviS1Hd9Ob0BGBka3DdVMwDfly/i3o+eEew8Ceqhvad9m2cSnqSGDHa0L/Z+jWxdFwLrEL27isrbS6p6mM6GMx7A8din3oJznHLS4/BwQJYSIdCqlml33Y0BHRFR+joFSwAGNn8DRMYD5+MFxD579qg6tg9dg88iCcd+zve4GTE0dHLfdGACGcg+CeEDOP5iqI93oVZPw7cFlo9cY6DkbH4AzJwMDR4HhsQA4EQ/2UQckTkGD7Xo/j0FxOQISv+ccNq/3pJICU6KIeA3oOFiciCgGohqoXggce/qyUBhr4rGxq8fT9zuWGM5dhp1zvoHXMQUjStA9Mtk2mAOARhkfzOW2Hxr9dyhD5f0OmzYM7BYoNMlBfLvue/hkanvwA+bnLss9tLf1AV/bD3zyX0IfvB64sId7G7kNU/fTnCTMQe1O4ty1UueeRPlzQFRluIaOiCgGohqo7reJh9OcwI1dPbh55/uRHbzD07n0qsmYahHU9apJxV8HdA8KmclnsgeQEosdvD4gWzyYZvAH3DHlJ8CKNf5P1EkSZwRGGZC4NeaxXZeocpkwp4ybbtOfoLJ55e5a6XQdOvckzoEpUcIxQ0dEFAN2g9ODGqi+savHtlwS8B40rVw0C5l0TdG2wpxAq2DRydqhZciquqJt/aoOa4eKHwQ934M9HbmH8raG3N+GLIExM9mrJlt/v9cHZN0HU4fzqgp29zWMgMTts7loVa7Mz3Ifl4ybzuceZDbP6pyj6loZZMYzyp8DoirDgI6IKAacAiW/jMGMHa9B09J5TVhz2Rw0NWQgQFGJoVNQWNj3qvOmj35v50kXY+/Z3xwtIezPvA+r1PKiEk3P92BPB4Y2XV/04Dm06frRB09jsLl2aBn6TYGk+QG5EADPbN2ClvZtxSWpOg+m5SrTi5NSApJSg2C3z2buslyZ6sRp1vs5lQDqfO5BlhcWnbNNqW1YvzRwuw6de1LOwJSowrHkkogoBsIcqO6WOdMNHO3mBNqVYxZ1fNzTAexfDUzoBo6bCsxYBSzJNXaoB7Cgqwe/LOEe9P90FeqHjxVtqx0+lts+d1lRsLl5ZAEwCNxU24FGOYRUQ3EZmeuwcJ0W9FU4m28c3Tb6fgbUe/lsCmWrbQ0ALBrD2WWddD73oMsLnUpt/dwvN14ynl7vCccpEIWGAR0RUUyENVDdKXPW5DNwNHbMnJhJI10jGBwee0guChY9PHiWeg8mZF933G4ONjePLMDmgQW5YHNF8XgB13WGGg+m6kg3rJbr2W2vWDpr//wEwTpBg+7atDDf248wf2ngdh26QVoS14ASJQADOiKiCucpc1YCcyarLzuIdEpwcn0aff2D4zNsIT549o5MshyB0DsyCVORK2m1GgthlZn01KDG44Pp7zEZp+BNm+1kqZQ1iuaAwks7/1KGfXsNSLy8d1BNU8JsNqKT8SSisuEaOiKiChfW+jyrTNbgiEJ9XS32ty/GjtaFxdm2EB88N9RdNW5dXL+qw4a6qwA4r/0zC7JBzZqBT1ue15qBT2u/V9WIao2il7VppXJ7by/n7XVdXJjNRsK8R0lS7Y2NKPZcM3Qi8n0AnwDwhlJqtsXrVwL4Wv7LowD+Win16/xrrwJ4B8AwgCEvg/GIiChYYa3P0x61EGIZ2pmLl2PVo0O4Uf0IjXIIvWoSbscVWLB4+eg+Xss5dbJ5bnaddDFa3x5br9erJmHt0DJ0nnSx9nuVnU5GyU/2Kco1imFml5ze2+283cqTzQPna+rGD5wPqtlItWfgwlyjSBQQLyWX9wH4LoD7bV7fD+AjSqm3RORjAO4FcK7h9QuVUtbTY4mIKBJhrM9zmklnqZQSN49y13YtPrP1It9Ba5ABcC44HMDmgeLOnWsC6F4aNON6yHHXrPNQ6/cBWGddVlJnm7mdt1t3SeP9zR4GUmkg8x4g+5b/ZiNBlYJWCjY2ssafk1hxDeiUUj8XkRkOr//C8OWvAHCgCBFRFdDOZIXc5S7IoDWo9wqze2mQXDt76jzUBvEA7DUrVO6h26VyO2+ngM/q/o4MAnXHA1/b7++8kpSNiiqgSOovDcKUpJ+TKhF0U5QvAvip4WsF4AkRUQDuUUrda/eNIrIcwHIAmD59esCnRUREQSspWPFRvuWYQYqxsLqXlsLuHrp29tR5qI3yATjErG+o3M7bKeAL8/4mJRsVZUCR1F8ahCkpPydVJLCATkQuRC6gW2DY3KKU6hWR9wJ4UkReVEr93Or788HevQDQ3NxsMRiGiIjiJqpgxTWDFLEkBpdO99Bq3eOS1Hbc1N8BtB0CJAUoi1mGVg+1UT4AJ3W2mdt5OwV8T632f3/tsltJyUZFGVAk9ZcGYUrKz0kVCSSgE5G5ADYA+JhS6lBhu1KqN//3GyLyKIBzAFgGdERERHZcM0gRiltw6cQYeKZEsFiewU11HWiUg+hVk7F2aBnWba0btx5ySWo72tMbUC/5RhtWwZzdQ62HB+BAA+KkNu1wOm+3gM9PgOGU3UpKNirKgCKpvzQIU1J+TqqI74BORKYDeATA55VS/2XYfjyAlFLqnfy/LwGw2u/xiIio+mh31AxRnIJLJ+bAc7E8UxSkTZWDaE9vwM1vAxd++rqifW+q7RgL5oykBlAjzg+1Lg/ASQqIy8ou4PMbYDhlt5KSjYo6oEjqLw3CkpSfkyriZWzBDwFcAGCyiHQDuBVAGgCUUncDWAVgEoC7RAQYG0/wRwAezW+rBfCgUuo/QrgGIiKqcNodNUMUdHAZVvmmOfC0CtLqZQArazvwma0X41NnN+HpF9/MnUfqkPntctQI0NbnfnCHB+CkBMSx5ifAcMpuJSUbxYCivJLyc1JFvHS5/KzL69cAuMZi+ysAPlT6qREREeUEORvOryCDyzCzVeYAs1GsJwg1yiH09GXxcGfP2LD128LLgMQp2xorUXVtdMtuJSEbxYCi/JLwc1JFUuU+ASIiIisbu3rQ0r4NM1u3YN3WffjU2U1oashAADQ1ZPCps3PdGWe2bkFL+zZs7OoJ5Fhu77Vy0Sxk0jVF20oNLu2yVX/T8Wvf12UOMHvVZMv9etWk0eOu27ovt/GiVbmMh1FAGRC7wLcc2dbYKKxrO3IAgBpb17anI/hjhfjZRmruMmDF3lzGeMXe3LbbZgNtDbm/w7h3RDHFgI6IiGKnkLnq6ctCAaMZpJWLZmF/+2KsXDQLD3f2FL1+8yPPlxT8WB3L6b2WzmvCmsvmFAWXo5kt6AWHdlmpYaV8X5c58Fw7tAxZVVe0T7+qw9qhsd+yj57P3GXApeuBidMASO7vS9cH8hv5lYtm4fK6X2B73Q145bjPYXvdDbi87hdlybbGhtsg8SCF+NmWjZeAeE8HAz6qWKJU/CYENDc3q127dpX7NIiIqExa2rdZljU2NWSwo3Wh6+tBHkuHuYQSyGXvjAGfl2MHcS6F8zGuz7v9jJcw/+U7MXKkG70jk7B2aBk2j4xNGyr1OFr2dGBo0/WoHT42ummoZgJqP3lnsoMKP9oakBvdaybe1izGhd+y0VK//7bZNmWk03LZO3NnTyCXlUx6IFstoipHjiER6cz3JnEU9GBxIiIi39zWWQW5DsvLe3ltXKLb8MNqbaDOOboZPydwIYAvYXMh8Bwpw5rEp1YXBXMAcl+XYShxbOYJVkIb+FKGfRsf1DMnAwNHgeEB799f4DbGgIOwkyvKIfIJxpJLIiKKHbd1VkGuw3J7L52STN1A01y+WZPrDO35HEvlVjYaqpgMJdYttQ1VOde1BVWKqFs2ai6TzB4eC+aM3//ol93PzS7wLWyPyc+cb9VYNhplOXKCMaAjIqLYcWs8EmRjErf3csq6mZUSaC6d14QdrQuxv30x/nnZhwK7LjfG4+5oXRhdZsrt4TsiOp9r6Mq1ri3IZiy2QdMB6wDE6kHdihp2Pze3gDgmP3O+RNk4J04qJRgPGQM6IiKKHbcMUpAZJrf30sm6+Q00da9LpwFL0Eo+dky6LMZufIK5a2MU5WRBZj8cgyOLAKSUB3K7c3MLiGPyM2fJa9Yt7ExVXLN/lRCMR4Br6IiIKJbGr//Sez2oY+nMnSu8h591WV6vK8wZdqEeO+AZYuZ1cBeePmVsQLrD/Y/TsPqyCTL7YTXs28y4bs1u3aAbu3NzmosW17l1OuvDwsxUxXmdGofIe8Iul0RERA5d1HQ7V7oJqhFHkN05k3RsI6vPxszuswr6c00kt+6Quoz/HVl27QRGO3dadZ5MpYHjTgSybwGSypdbBnRucaRz/4P+rKJ67yCwyyW7XBIRETly+e10EFm3giCzauUsGYxLuaLVOjgzuy6jQX6uiRV09sOYJbMNEqaO7QvYP6jbjRqopMyMTtYtzExV3NepOWVfCQADOiIiqnYeWpoHVd6pO9bASTlLBuNSrug1gHTqMhpUABebEQg6wixF9BKAJLFMMkg64yrCvB+VMDajyjGgIyKi6hbhb6eDzGxZzbCLapZcOY9tZBdYWu1XEEbgVc71jIXjO16TU8laWNmPIAKQSs/M6GbdwrofXKeWeAzoiIioukX42+kgM1vlLBmMS7mil8HsxkAzrMAryMyrLtdrKmfDiyADEN11VElYdxWXLGRczoNKxqYoRERU3ezW6oQwByzJjTjKVVLodlydLpdhNXOZ2brFsgWIANjfvrjk9/XC9Zri3vDCC93/RiP8b3r0eAyGKARsikJERORFhL+djktmS1e5Sgq9HFdnHVxYzVyCyLyWGjC7XlPcG1544WGdq6/9/Yhzy3+qGgzoiIiIIlyrE2QjjqiUq6Qw6OOG1czF75pCPwGz6zVplhTHsrmLblAaZRAbZfBIZCNV7hMgIiKi+NnY1YOW9m2Y2brFtvFI2GMKgs6orVw0C5l0TdE28xq7wjW3tG/Dxq4eT++7dF4T1lw2B00NGQhy5Y46ZbROgavfa8JFq3LlhkY2DS8KgWVPXxYKY4Gl1/sQGrv1rEFt9yPJGdA9HbmS3LaG3N97Osp9RlQiZuiIiIioiJeB3UD4YwqCzqg5lbz6LSv1k3n1E7i6lvFqlBSXs7mLI90ujFF2bUxqy/9ylopyzWHgGNARERFViKDK5bwM7NYtKSzlvMIYj2AXeJUzmPEbuLoGkx5LiuMyMH4c3XWuUXZtTGrL/3KVinLNYSgY0BEREcWUTiAUZOMSpwd4AbSCMi/nZXedUTaRKWcwE/e5flEPjLeku841qnWxSW35X65SUa45DAUDOiIiohjSDdCCzDDZPdiX0t7f7bzcrtNvExmvQXHUwYz5vD51dpPtuIWoxCWwLDvdksByztsr9b0lBSiLLHzYpaJJXnMYY2yKQkREFEO6jTKCzDC5NtrQ4HZefhqCuNFp8hHkNZdyXg939mDlolnY374YO1oXlmXNmt/mLhWhUBJ45AAANVYSGEXDkDCPbX5vq2AuilLRoBvWsLELAI8BnYh8X0TeEBHLCZSSs15E/ltE9ojIWYbXrhaRl/J/rg7qxImIiCqZboBml0kqJcMU5IO923mFWeqoEyxGGcyEGcR64dTNc+m8JuxoXVjWwLKsnEoCS6DVOTXgY7u+NwBIDQDJDZoPa/C6kUbXVVflDL5jxmvJ5X0AvgvgfpvXPwbgtPyfcwH8K4BzReQ9AG4F0AxAAegUkc1Kqbf8nDQREVGl0y0BDLpcLqh5eW7npXudOusKdYPFqGYElnO9XrmGxCeG35JAQ1ljf+YUbH/3U+gZ+DAAD/c6zHJEu/dQI0Bbn//3NzKXjZ52CfDSE2Nff+hzxV+XWlbK9XijPGXolFI/B3DYYZdPArhf5fwKQIOIvA/AIgBPKqUO54O4JwF81O9JExERVTqrEsB0StA/MGSbWYljuZzbeemUOurOSQsyaxmkcp5XubODseenJNCUMarPvobVci+WpLaP7uJ4r/2WIzqVH0Y1m88qa7bre8Vf//rBXBDX1ges2Ft68MX1eKOCaorSBMA4hKM7v81u+zgishzAcgCYPn16QKdFRESUTM2k3MgAACAASURBVOYOjxMzabw7MIS3+gcBWP+2P6oMky6n89LpZKnb+KWcTT6cMonlPK/YjiaICz9jCCwyRvUygJtqO7B5YMHoNtt77efYbuMAohqvYFfaaRRUFi2pMwBDEFRAJxbblMP28RuVuhfAvQDQ3NxsuQ8REVE1MQZCLe3b0JcdLHo9FkOfA+A1EC2lhBKIZuyBkZfOneU4LyDmowniwM8YApvMUKMcKv7a7l5bHfu0S3JfP7Lc+Vzcyg+jGq/gNTsWRBYtqTMAQxBUQNcNYJrh66kAevPbLzBt/1lAxyQiIqoacc6sBDXQ3E0pwUg5spZeMolRnpfx85mYSSNdIxgcHvvdeVWOJnBS6hgCm4xRr5o0+u/L636B1fIw0Pa6dVBlPLbOEG4v5YcBjlew/W/eLmtmFkQWrZQgNcyxEGUUVEC3GcB1IvIj5JqiHFFKvSYiWwF8S0ROzu93CYCbAzomERFR1YhrZiXKJhtJmZNmF2T39GUxs3WLdtBrfni+8PQpnmfWmT+fvuwg0inByfVp9PUPBhqARxXYx5ZFxmioZgI21F4FGQCuPuFZ3KI2oDZ7LPeiU4AG6DX9iLD80PG/eausmVmQWTSdIFUnQE4Yr2MLfgjglwBmiUi3iHxRRL4sIl/O7/I4gFcA/DeA/w/AtQCglDoM4B8A7Mz/WZ3fRkRERBqinJOmI8omG3Ft/GLmFGR7aeZiZNUI5oFf/c5zYxirz2dwRKG+rjbQ0QS6DWsK3+O5pX8SzF2Wa/0/cRoKowBqP3kn2m75Bva3L0bb8Q+jdvhY8fc4jSXQafoR5DgAwLHBiuN/8xb3AM1fLP46ivEIVsIcC1FmnjJ0SqnPuryuAHzF5rXvA/i+/qkRERFRQTnXXTmJuhQ0ro1fjKwyiWZe1z9aPTzrvFdUn49uw5qwM7tlyxY6ZYx0uzLqZN2CXCPnksly/ZnSLe2MqgzS9v4fyAWuCS7BDKrkkoiIiEIWx2DGrhQ0JVJSeWElMAffdp3evARVXgMvp4HzgZbq2jx86waOugGgjtjO2tMti9Rt+mEOpApZNt1AyaXUM9CfqSjLIB3X96lEl2B6KrkkIiIismJVCgoAw0pplxfGjbkk8JaNz3suEVw6rwk7Whdif/tiNJUwd65wbK9tv50GzgdWqms1Y+wnNwB7OrRn67mtM/RTghnbWXu6ZZFW5YteyxUdPitXLpnEQH+m7ILHR79sPU/PD6v7b5bQEkwGdERERFQy87q2Ghk/sSgWD9Oa/K5dM9J9ADYe2wun9wp03aFD5kb3GoNcZ2gW246wpQRoc5flhm/rDuH2s17MZQh5oD9TdsGjGoZ2IAo4D1c333/dc4oxyS1/i5fm5ma1a9eucp8GERERaZrZusUyqyQA9rcv9vQeceiW2NK+zVNA1dSQwY7Wha776VyT07GbNLtcBqqtAdbjhAVo69O6RnNZpB2v99fI7v6V8l6J5fJZOTKXQQK5zFYYzUxum+1xzMG0XEDrRPe87Y7t5VgREZFOpVSz235cQ0dERESB8bu+Ji7rn/yuXTPTWf9o954ClDcgcVkDpnONQa4zNIvzeAs/Iyi0aK7XKz6vybh9zjcw/+U7w29U4mXMAeAta6Yz5sHu2AkdTM6SSyIiIgqM3/U1cVn/5DUADWMOoO56tMhYrEEaqpmAtnc/VdK6N7/rDJ3eV6sk0KlML0BBlvG6nrfGej2r8/rCzvdj4wVbx0o9gXDukbkMUsavxwXgaZ6esgn67Lb7WqMYM8zQERERUWD8jleIy/onL6MHwsr6RJlh0ipvNbXG78+cglXvfgoPDZwDwF82Nehr9pwtjLDLot8RFEXczltjjIFrx9Gw75HxfO3KJj1kzX6PyTgFb9ps93DsBGNAR0RERIHyM14h8Db7JbIKTKNauxbVzMGSylsND8AXt29Dz0DxZ1Xq6IGwr9k2cPVQphfUms5Ay3i9lBd6DFZcf4miW8roh495emsGPo016Q2ol4HRbf2qDmsGP407gj3L2GFAR0RERLERp/VP5Zz7F8Wx/c6CCzqbGtY1OwauLi36g1zTaffLCqv9XOkOKXc5nuMvUbwcK8jh4CVmzXaddDFa3wZuqu1AoxxCr5qEtUPL0HnSxaWdR4JwDR0RERHFRqAt0cmR34Astmv9TKwC14uH/zfO2/QRWHeCxOiarSDXdNrNbDTy/MsLl9ECfs+r6DzcjuVn5l2AVi6ahSdrPoIFA+vxgT/8AAsG1uPJmo/EohlO2JihIyIiolgpZ2asmngpb3Xqyjgxk0a6RjA4PBYUxaWbpJE5QF2S2o729AbUY8By/6yqw95Tr8d8i++1e08vAi3jDbBDo2u5q9uxoizJdGB1Hbef8RLm/+xvgU0hd+ssM86hIyIiIqpCVrPgMuma0Yyol1lx6ZTghAm16OsfLNvMQDfmuXTb627A1NTBcfspBfSoyaNlejtaF9rOtKsRwYhS0a31sxJkmaMbp2P5mXkX5HlY7RvVPL2QcA4dEREREdlyy8x46co4OKJQX1eLrlWXhH6+pTKvy2yU8cEcACgIFgysBwBIPoiz63Y6nE+IuK6p0whAtNfr+ezQqN3h1O5YmjPvSqbbbTMmmcMoMKAjIiIiqlJO5a1BD1cvF3Pg+oZMsWxv36smjf67UHZq/t6UyGgwV2DbSEYzAPHbpEZHkM1eIhvQrRugBdg4Ju4Y0BERERHROIF2ZQyY09o+q2xTUeC6510MbboetcPHRl/vV3VYO5QLCszrAI3fO7N1i+X5WAa1mgFI2DMYjfdMKzB142PUgBbdAC2qzGEMsMslEREREY0TaFfGABWySz19WSjksksP/Op3RV/f/Mjz2NjVY/39wy1oHbwG3SOTMaIE3SOTcfPgNdg8ssC1q6pWZ0/NACTMrqHme2YO5gpKDh7nLgNW7M2tmVuxN5ySRt3OnhetymUKjcLIHMYAM3REREREJQhq4HRcBT1cPaj75WVtn1O2ad3WfegZ+DAewoeLtjc1ZLCjdaHj+2rNSdTMEIU5g9HLPQPiN3KiiG5pZ1SZwxhgQEdERESkKdA1SDEW1AiJIO+X37V9fkobXVv8G2kGIHYB9Lqt+7Dix7t9BcFeri2OIyeKlBKg+WwckxQM6IiIiIg0RdnAohIEeb/8ru3zMn/Piecgt4QAxPjeQQbBdtcc1viFsGwcbsG6P6xH77EsGidksHJ4FpaW+6RigAEdERERkaawG1hUmiDvl90oASOnbFOYpY3j+MgQBRkE212z03rBuKmWrHgpGNARERERafKb5akGbl0VgdLul9+1fVplk1YiGujtNwg2r1n81NlNJa9/jIMwsuKVsg6WAR0RERGRpkizPAlkzqZYBXN+7pfftX0lf7/ucGsf/PzSwCqb9XBnTywycqUGUUFnxSsp4+cpoBORjwK4A0ANgA1KqXbT67cBuDD/ZT2A9yqlGvKvDQN4Pv/a75RSS4I4cSIiIqJy8Z3lCVEcsg52XRVLXbMV5TU5Hkt3uLUPur80CG3OXID8BFFBZ8UraR2sa0AnIjUA/gXAxQC6AewUkc1KqRcK+yilVhj2vx7APMNbZJVSZwZ3ykRERETlF1QHyCDFJetglzUZUQr72xdrvVeU1+R6LN3h1j7o/NLAS0YUKP8aTz9BVNBZ8UpaB+slQ3cOgP9WSr0CACLyIwCfBPCCzf6fBXBrMKdHRERERF7FJesQZDYlymtyPZbmbDm/vP7SoNxz5rxmUCMbGeFBJa2D9RLQNQEw/uR2AzjXakcReT+AmQC2GTZPEJFdAIYAtCulNtp873IAywFg+vTpHk6LiIiIiIziknUIMpsS5TW5Hkt3uHVEwp4z5xSw6WRQIxsZ4UElrYNNedhHLLZZ53GBKwA8pJQy/opgulKqGcDnANwuIqdafaNS6l6lVLNSqnnKlCkeTouIiIiIjJxmr0Vp6bwmrLlsDpoaMhAATQ2ZkhtyRHlNrseauwy4dD0wcRoAyf196frR9XMbu3rQ0r4NM1u3oKV9GzZ29QR+jjrnXSPi+/4XAraeviwUxgK2wrU5ZTXNVi6ahUy6pmhbuYKoIH9Gy81Lhq4bwDTD11MB9NrsewWArxg3KKV683+/IiI/Q2593cvaZ0pEREREjuKUdfCbTSlkhXr6shAUZxPCuiZP989mtlzYa/2csmRhzplzK0PVyaDajZxYt3UfVvx4d+RNfOK4DrYUXgK6nQBOE5GZAHqQC9o+Z95JRGYBOBnALw3bTgbQr5T6g4hMBtACYG0QJ05ERERExeLcfVOHOThSwGhQ1xTiNfm5f2Gu9XMLFsP83N0CNt0ySuP5xqWJT9K5BnRKqSERuQ7AVuTGFnxfKfUbEVkNYJdSanN+188C+JFSRW11PgjgHhEZQa68s93YHZOIiIiIglUJWQer4KgQzO1oXRjqsUu9f2Gu9fMSLIb1ubsFbFbZQUEuOGtp3+YYWMaliU/SeZpDp5R6HMDjpm2rTF+3WXzfLwDM8XF+RERERFRl4tLcRUeYXRPLeT/cylCN2UFzeaxbxi2Jn3MceWmKQkREREQUmbg0d9ERZsOPct4PL81Dls5rwo7WhWhqyIzrnGjXIAVI5uccR54ydEREFB+Dg4Po7u7GsWPHyn0qRCWZMGECpk6dinQ6Xe5ToZiKU3MXr8Jcx1bu++G1nNMus9bTl8XM1i2em7nE+XOOIwZ0REQJ093djRNPPBEzZsyAiNVkGaL4Ukrh0KFD6O7uxsyZM8t9OhRTSW3uEtY6tqTcD7uyUwBFIw+A8Ju5VBMp7mESD83NzWrXrl3lPg0iolj67W9/i9NPP53BHCWWUgovvvgiPvjBD5b7VIgSy2mMQTnPyZxxs6LT3CaO1xkVEenMz/N2xAwdEVECMZijJOPPL5E/cW33b8642aWNvDY9iet1xg0DOiIiIiKiEpUjgxTndv/GUsqW9m3anT+N9zMlgmFTNWFcrjNO2OWSiIgo4V599VXMnj07lPf+2c9+hk984hMAgM2bN6O9vT2U4xAlUSGD1JPPRhUySBu7ekI9blLa/et2/jTfT3MwV1C4zo1dPWhp34aZrVvQ0r4t9PseVwzoiIgqXNT/h/fxj38cfX196Ovrw1133TW63RgYVLU9HcBts4G2htzfezrKfUaeLVmyBK2treU+DaLYcMqUhSkp7f69jDwwsrqfVhobMmULpuOIAR0RUQUrx//hPf7442hoaBgX0EVpaGioLMd1tacD+MkNwJEDAFTu75/cEEhQNzQ0hKuvvhpz587F5Zdfjv7+fqxevRrz58/H7NmzsXz5chQaoa1fvx5nnHEG5s6diyuuuAIA8O677+Iv//IvMX/+fMybNw+bNm0ad4z77rsP1113HQDgL/7iL3DDDTfgwx/+MD7wgQ/goYceGt1v3bp1mD9/PubOnYtbb73V97URxVW5MmVhzrwDgv1FYGFG3f72xdjRutCxVNLLfStcp5dguloyeAzoiIgqWBi/PV67di3Wr18PAFixYgUWLsx1Knvqqadw1VVXYcaMGTh48CBaW1vx8ssv48wzz8TKlSsBAEePHsXll1+O008/HVdeeSWcOi3PmDEDt956K8466yzMmTMHL774IgDg8OHDWLp0KebOnYvzzjsPe/bsAQC0tbVh+fLluOSSS/CFL3wB9913H5YuXYpLL70UM2fOxHe/+1185zvfwbx583Deeefh8OHDJd+Dkj21Ghg0PbAMZnPbfdq3bx+WL1+OPXv24KSTTsJdd92F6667Djt37sTevXuRzWbx2GOPAQDa29vR1dWFPXv24O677wYA/OM//iMWLlyInTt34umnn8bKlSvx7rvvOh7ztddew/bt2/HYY4+NZu6eeOIJvPTSS3j22Wexe/dudHZ24uc//7nv6yOKo3JlynQzXzrKmfmyu281IuOu0y2YrqYMHgM6IqIKFsZvj//sz/4MzzzzDABg165dOHr0KAYHB7F9+3b86Z/+6eh+7e3tOPXUU7F7926sW7cOANDV1YXbb78dL7zwAl555RXs2LHD8ViTJ0/Gc889h7/+67/GP/3TPwEAbr31VsybNw979uzBt771LXzhC18Y3b+zsxObNm3Cgw8+CADYu3cvHnzwQTz77LP4u7/7O9TX16Orqwvnn38+7r///pLvQcmOdOtt1zBt2jS0tLQAAK666ips374dTz/9NM4991zMmTMH27Ztw29+8xsAwNy5c3HllVfigQceQG1trj/aE088gfb2dpx55pm44IILcOzYMfzud79zPObSpUuRSqVwxhln4Pe///3o+zzxxBOYN28ezjrrLLz44ot46aWXfF8fURyFnSlzopP50lGuMlLA/n7+87IPjbtOt2C6nNcRNQZ0REQVLIzfHp999tno7OzEO++8g+OOOw7nn38+du3ahWeeeaYooLNyzjnnYOrUqUilUjjzzDPx6quvOu5/2WWXjR6zsO/27dvx+c9/HgCwcOFCHDp0CEeOHAGQW+OVyYxd24UXXogTTzwRU6ZMwcSJE3HppZcCAObMmeN67FBMnKq3XYN5FICI4Nprr8VDDz2E559/Hn/1V3+FY8eOAQC2bNmCr3zlK+js7MTZZ5+NoaEhKKXw8MMPY/fu3di9ezf+L3v3HR93def7//XRqHfbKla1bNwbBmSaaQkBm1BDEgIku5ByIfsLIZXdkL0hhE3ukmT37i+7m8feZXMJbAqOkwAxJoEkJJCEZhuMZWzjgouqJVm2ep8594/vSBo1W9ZIGpX38/HwY/T9zpmZM18GSW+dcz6ntLT0tPvExcXF9X7dM9rqnOP+++/vfZ6DBw/yyU9+Muz3JzIZjedIWaREsuDKmVzP04Xp4fpbUd827aZgKtCJiExj4/HX45iYGIqKivjhD3/IxRdfzKWXXsof//hH3n333TMKAD6f77Rr3Xrah7YdappmT5hJSkoa9vWioqJ6j6OioiKzzu7KByBmQJiOSfDOh6m0tJRXX30VgCeeeIJLLrkE8EY5m5ube9e4BQIBysrKeM973sN3vvMd6uvraW5uZv369fzbv/1b7/XdsWPHqPqxfv16Hn30UZqbmwGoqKigpqYm3LcnMmmN10hZpES64MpIr+fpwt+p+jvdpmBqHzoRkWls4CavY7VH0mWXXcY//dM/8eijj7Jq1Sq++MUvct555/UbJUpJSaGpqSms1xnutX/yk5/wta99jRdffJGMjAxSU1PH/HXGxepbvNsXHvKmWable2Gu53wYli1bxuOPP87dd9/NokWL+Ju/+RtOnjzJqlWrKCoqYu3atQD4/X4+9rGP0dDQgHOOL3zhC6Snp/O1r32Nz3/+86xevRrnHEVFRb1r7s7E1Vdfzd69e7nooosASE5O5sc//jFZWVlhv0cRGX/3rV/SbzNvmLhppGcqdM+7gYZ6HwNNlz3tFOhERKa5U/3AG61LL72Ub33rW1x00UUkJSURHx8/aLrlnDlzWLduHStXruSaa67h2muvHZPXfvDBB/n4xz/O6tWrSUxM5PHHHx+T550wq28ZkwAXqqioiD179gw6/81vfpNvfvObg87/5S9/GXQuISGB//zP/xx0/oorruCKK64AvMqWd955J+BVvAzVMyIH8LnPfY7Pfe5zZ/AORGSyGK8/BE60ge9juBJck23vvtGwU1UYi5Ti4mK3ffv2SHdDRGRS2rt372mnNopMdvoci8hEWvfwH6gYIrzlpSfw8lfeG4EenZ6ZveGcKz5dO62hExERERGRaS2SFUnHm6ZciohIRH3gAx/g8OHD/c59+9vfZv369RHqkYiITDfTZSrpUBToRESmIOfcoDL1U9VTTz0V6S7IBJuMyz1EZPobjzXlk4GmXIqITDHx8fHU1dXpl2KZkpxz1NXVER8fH+muiIhMCxqhExGZYvLz8ykvL6e2tjbSXREZlfj4ePLzw99MXUREFOhERKacmJgY5s+fH+luiIiIyCQwoimXZrbBzPaZ2UEz+8oQ999pZrVm9lbw36dC7rvDzA4E/90xlp0XERERERGZyU47QmdmPuD7wFVAObDNzDY75wbuYPoz59w9Ax47G/g6UAw44I3gY0+OSe9FRERERERmsJGM0J0PHHTOHXLOdQIbgRtH+Pzrgd85504EQ9zvgA2j66qIiIiIiIiEGskaujygLOS4HLhgiHYfNLPLgP3AF5xzZcM8dshaoWZ2F3BX8LDZzPaNoG8TLQM4HulOzFC69pGl6x85uvaRpesfWbr+kaNrH1m6/pEzma79vJE0GkmgG2qjo4G1sp8BnnDOdZjZp4HHgfeO8LHeSeceAR4ZQX8ixsy2O+eKI92PmUjXPrJ0/SNH1z6ydP0jS9c/cnTtI0vXP3Km4rUfyZTLcqAg5DgfqAxt4Jyrc851BA//CzhvpI8VERERERGR0RlJoNsGLDKz+WYWC9wKbA5tYGY5IYc3AHuDXz8PXG1ms8xsFnB18JyIiIiIiIiE6bRTLp1z3WZ2D14Q8wGPOud2m9lDwHbn3GbgXjO7AegGTgB3Bh97wsz+AS8UAjzknDsxDu9jokzqKaHTnK59ZOn6R46ufWTp+keWrn/k6NpHlq5/5Ey5a2/ODbmkTURERERERCa5EW0sLiIiIiIiIpOPAp2IiIiIiMgUpUA3Ama2wcz2mdlBM/tKpPsz3ZlZgZn90cz2mtluM/tc8PyDZlZhZm8F/70/0n2djszsiJntCl7j7cFzs83sd2Z2IHg7K9L9nI7MbEnI5/stM2s0s8/rsz9+zOxRM6sxs7dDzg35eTfPvwZ/FpSY2bmR6/nUN8y1/66ZvRO8vk+ZWXrwfJGZtYX8P/B/Itfz6WGY6z/s9xozuz/42d9nZusj0+vpYZhr/7OQ637EzN4Kntdnf4yd4vfMKfu9X2voTsPMfHibpV+Ftw3DNuA259yeiHZsGgtWTc1xzr1pZinAG8BNwC1As3PunyLawWnOzI4Axc654yHnvgOccM49HPyjxizn3N9Fqo8zQfB7TwVwAfBx9NkfF2Z2GdAM/LdzbmXw3JCf9+Avt58F3o/33+V7zrkLItX3qW6Ya3818IdgQbZvAwSvfRGwpaedhG+Y6/8gQ3yvMbPlwBPA+UAu8HtgsXPOP6GdniaGuvYD7v9noME595A++2PvFL9n3skU/d6vEbrTOx846Jw75JzrBDYCN0a4T9Oac67KOfdm8OsmvG0w8iLbqxnvRuDx4NeP433jk/F1JfCuc+5opDsynTnn/oRXnTnUcJ/3G/F+AXPOudeAdOu/bY+cgaGuvXPut8657uDha3j718o4GOazP5wbgY3OuQ7n3GHgIN7vRzIKp7r2ZmZ4f8B+YkI7NYOc4vfMKfu9X4Hu9PKAspDjchQuJkzwL1PnAK8HT90THO5+VNP+xo0Dfmtmb5jZXcFz2c65KvC+EQJZEevdzHEr/X+g67M/cYb7vOvnwcT6BPCbkOP5ZrbDzF4ys0sj1akZYKjvNfrsT5xLgWrn3IGQc/rsj5MBv2dO2e/9CnSnZ0Oc0zzVCWBmycAvgc875xqB/wDOAtYAVcA/R7B709k659y5wDXAZ4JTQ2QCmVkscAPw8+ApffYnB/08mCBm9vd4e9v+JHiqCih0zp0DfBH4qZmlRqp/09hw32v02Z84t9H/j3n67I+TIX7PHLbpEOcm1edfge70yoGCkON8oDJCfZkxzCwG73+ynzjnngRwzlU75/zOuQDwX2i6x7hwzlUGb2uAp/Cuc3XP9ILgbU3kejgjXAO86ZyrBn32I2C4z7t+HkwAM7sDuA74qAsu9A9O9asLfv0G8C6wOHK9nJ5O8b1Gn/0JYGbRwM3Az3rO6bM/Pob6PZMp/L1fge70tgGLzGx+8K/mtwKbI9ynaS04f/z/Anudc/875HzofOUPAG8PfKyEx8ySgguEMbMk4Gq867wZuCPY7A7gV5Hp4YzR7y+0+uxPuOE+75uBvw5WPLsQr2hBVSQ6OF2Z2Qbg74AbnHOtIeczg4WCMLMFwCLgUGR6OX2d4nvNZuBWM4szs/l413/rRPdvBngf8I5zrrznhD77Y2+43zOZwt/7oyPdgckuWGnrHuB5wAc86pzbHeFuTXfrgL8CdvWU7QW+CtxmZmvwhrmPAHdHpnvTWjbwlPe9jmjgp86558xsG7DJzD4JlAIfjmAfpzUzS8Srqhv6+f6OPvvjw8yeAK4AMsysHPg68DBDf95/jVfl7CDQild9VEZpmGt/PxAH/C74feg159yngcuAh8ysG/ADn3bOjbSghwxhmOt/xVDfa5xzu81sE7AHbyrsZ1ThcvSGuvbOuf/L4LXToM/+eBju98wp+71f2xaIiIiIiIhMUZpyKSIiIiIiMkUp0ImIiIiIiExRCnQiIiIiIiJTlAKdiIiIiIjIFKVAJyIiIiIiMkUp0ImIyJRnZs3B2yIzu32Mn/urA45fGcvnFxERCYcCnYiITCdFwBkFup5Ne0+hX6Bzzl18hn0SEREZNwp0IiIynTwMXGpmb5nZF8zMZ2bfNbNtZlZiZncDmNkVZvZHM/spsCt47mkze8PMdpvZXcFzDwMJwef7SfBcz2igBZ/7bTPbZWYfCXnuF83sF2b2jpn9xIK7ZIuIiIy16Eh3QEREZAx9Bfiyc+46gGAwa3DOrTWzOOBlM/ttsO35wErn3OHg8SeccyfMLAHYZma/dM59xczucc6tGeK1bgbWAGcDGcHH/Cl43znACqASeBlYB/xl7N+uiIjMdBqhExGR6exq4K/N7C3gdWAOsCh439aQMAdwr5ntBF4DCkLaDecS4AnnnN85Vw28BKwNee5y51wAeAtvKqiIiMiY0widiIhMZwZ81jn3fL+TZlcALQOO3wdc5JxrNbMXgfgRPPdwOkK+9qOftyIiMk40QiciItNJE5AScvw88DdmFgNgZovNLGmIx6UBJ4NhbilwYch9XT2PDPCdqAAAIABJREFUH+BPwEeC6/QygcuArWPyLkREREZIfzEUEZHppAToDk6dfAz4Ht50xzeDhUlqgZuGeNxzwKfNrATYhzftsscjQImZvemc+2jI+aeAi4CdgAP+1jl3LBgIRUREJoQ55yLdBxERERERERkFTbkUERERERGZohToREREREREpigFOhERmTSCBUaazaxwLNuKiIhMV1pDJyIio2ZmzSGHiXjl+v3B47udcz+Z+F6JiIjMHAp0IiIyJszsCPAp59zvT9Em2jnXPXG9mpp0nUREZKQ05VJERMaNmX3TzH5mZk+YWRPwMTO7yMxeM7N6M6sys38N2Scu2sycmRUFj38cvP83ZtZkZq+a2fwzbRu8/xoz229mDWb2b2b2spndOUy/h+1j8P5VZvZ7MzthZsfM7G9D+vQ1M3vXzBrNbLuZ5ZrZQjNzA17jLz2vb2afMrM/BV/nBPA/zWyRmf3RzOrM7LiZ/cjM0kIeP8/Mnjaz2uD93zOz+GCfl4W0yzGzVjObM/r/kiIiMlkp0ImIyHj7APBTvM27fwZ0A58DMoB1wAbg7lM8/nbga8BsoBT4hzNta2ZZwCbgvuDrHgbOP8XzDNvHYKj6PfAMkAMsBl4MPu4+4EPB9unAp4D2U7xOqIuBvUAm8G3AgG8GX2M5sCD43jCzaOBZ4CDePnsFwCbnXHvwfX5swDV53jlXN8J+iIjIFKJAJyIi4+0vzrlnnHMB51ybc26bc+5151y3c+4Q3sbdl5/i8b9wzm13znUBPwHWjKLtdcBbzrlfBe/7F+D4cE9ymj7eAJQ5577nnOtwzjU657YG7/sU8FXn3IHg+33LOXfi1JenV6lz7j+cc/7gddrvnHvBOdfpnKsJ9rmnDxfhhc2/c861BNu/HLzvceD24EbqAH8F/GiEfRARkSkmOtIdEBGRaa8s9MDMlgL/DJyHV0glGnj9FI8/FvJ1K5A8ira5of1wzjkzKx/uSU7TxwK8kbGhFADvnqJ/pzLwOs0F/hVvhDAF74+wtSGvc8Q552cA59zLZtYNXGJmJ4FCvNE8ERGZhjRCJyIi421g9a3/BN4GFjrnUoEH8KYXjqcqIL/nIDh6lXeK9qfqYxlw1jCPG+6+luDrJoacmzugzcDr9G28qqGrgn24c0Af5pmZb5h+/DfetMu/wpuK2TFMOxERmeIU6EREZKKlAA1AS7B4x6nWz42VLcC5ZnZ9cP3Z5/DWqo2mj5uBQjO7x8xizSzVzHrW4/0A+KaZnWWeNWY2G2/k8BheURifmd0FzDtNn1PwgmCDmRUAXw6571WgDvhfZpZoZglmti7k/h/hreW7HS/ciYjINKVAJyIiE+1LwB1AE95I2M/G+wWdc9XAR4D/jReEzgJ24I2AnVEfnXMNwFXAB4EaYD99a9u+CzwNvAA04q29i3feHkH/A/gq3tq9hZx6minA1/EKtzTghchfhvShG29d4DK80bpSvADXc/8RYBfQ6Zx75TSvIyIiU5j2oRMRkRknOFWxEviQc+7Pke7PeDCz/wYOOecejHRfRERk/KgoioiIzAhmtgFvqmI7cD/e1gRbT/mgKcrMFgA3Aqsi3RcRERlfmnIpIiIzxSXAIbwpjxuAm6ZjsRAz+0dgJ/C/nHOlke6PiIiML025FBERERERmaLCGqEzsw1mts/MDprZV4a4f56ZvWBmJWb2opnlD/U8IiIiIiIicuZGPUIXXFC+H6/SVzmwDbjNObcnpM3PgS3OucfN7L3Ax51zf3W6587IyHBFRUWj6peIiIiIiMhU98Ybbxx3zp1qix0gvKIo5wMHnXOHAMxsI94C7D0hbZYDXwh+/Ue8Us6nVVRUxPbt28PomoiIiIiIyNRlZkdH0i6cKZd5eHvf9CgPngu1E2+fHoAPAClmNmeoJzOzu8xsu5ltr62tDaNbIiIiIiIiM0M4gc6GODdw/uaXgcvNbAfepqsVeGWiBz/QuUecc8XOueLMzNOOLIqIiIiIiMx44Uy5LAcKQo7z8TZp7eWcqwRuBjCzZOCDzrmGMF5TREREREREgsIZodsGLDKz+WYWC9wKbA5tYGYZZtbzGvcDj4bxeiIiIiIiIhJi1IHOOdcN3AM8D+wFNjnndpvZQ2Z2Q7DZFcA+M9sPZAPfCrO/IiIiIiIiEjQpNxYvLi52qnIpIiIiIiIzlZm94ZwrPl27cNbQiYiIiIiITElP76jgu8/vo7K+jdz0BO5bv4SbzhlYtH/yU6ATEREREZEZ5ekdFdz/5C7auvwAVNS3cf+TuwCmXKgLpyiKiIiIiIjIlNHR7eedY41845ndvWGuR1uXn+8+vy9CPRs9jdCJiIiIiMi00t7l51BtCwdqmjhQ3ezd1jRztK4Vf2D4GiKV9W0T2MuxoUAnIiIiIiJTUlunn3drm0OCWzMHqpsoPdFKT27zRRnz5iSyOCuFa1flsDArmW89u5eapo5Bz5ebnjDB7yB8CnQiIiIiIjKptXR0e8Gtupn9NU0cDIa3spOt9BTtj44y5mcksTw3lRvW5LE4O5lFWSkUZSQSF+3r93zO0W8NHUBCjI/71i+ZyLc1JhToRERERERkUmju6OZAtTc98mBwtG1/dTMVIVMhY31RLMhMYnV+Gh88N59F2cksykqmKCOJGN/ISoT0FD6ZDlUutQ+diIiIiIhMqIa2Lg7WNHOwxgtsB2qaOVjdRGVDe2+b2Ogozsr0wtri7GQWZqWwKDuZebMTiR5hcJvKtA+diIiIiIhEVH1rZ3Bdm7fO7WBNM/urm6hu7Fu/Fh8TxcKsZC5YMIeFWT0BLoWC2Yn4oiyCvZ8aFOhERERERCQsJ1o6e6dK9t7WNFMbUngkMdbHwqxk1i3MYHF2CouyvDVuebMSFNzCoEAnIiIiIiKn5ZzjeHNn70hb73YA1c3UtXT2tkuOi2ZhVjJXLM4Mrm/zpkrmpiUQpeA25hToRERERESkl3OO2qYODgSnR3rr27zwdrK1q7ddSnw0i7KSuWp5tjdVMjjqlpMWj5mC20RRoBMRERERmYGccxxrbO/dv623QEl1E43t3b3t0hJiWJydzIaVOb3r2xZlJ5OVEqfgNgko0ImIiIiITHFP76gYtgS/c47KhnZvbVvPNMngqFtTR19wm5UYw6LsFG5Yk+tNk8xKZmF2MpnJCm6TmbYtEBERERGZwp7eUTFok+zoKOPcwnQ6ugMcrGmmpbPvvozkOK8gSXD/tp6pknOS4yLRfRmGti0QEREREZmmmtq72Hesib3HmvjHX+/tF+YAugOO7UdPcvFZGXy4uKCvOElWMrOSYiPUaxkPCnQiIiIiIpOUP+A4WtfCO8ea2FvVyN6qJt451kj5ybbTPtY5+PGnLpiAXkokKdCJiIiIiEwC9a2dvYHtneDtvuom2rsCAPiijAUZSZxTOIvbzi9kWU4KS+em8uH/8woV9e2Dni83PWGi34JEgAKdiIiIiMgE6vIHOHy8hb1Vjb0jb+9UNXGssS+UzU6KZVlOCh+9YB7LclJZOjeFhVnJxMf4Bj3ffeuXDlpDlxDj4771Sybk/UhkhRXozGwD8D3AB/zAOffwgPsLgceB9GCbrzjnfh3Oa4qIiIiITBW1TR29I257g7cHa5rp9HujbjE+Y2FWChefNYelwRG3pTkpZ1RZsqea5XBVLmV6G3WVSzPzAfuBq4ByYBtwm3NuT0ibR4Adzrn/MLPlwK+dc0Wne25VuRQRERGRqaSj28/BmubeqZI9UyePN3f2tslOjesNbMvmprIsJ5UFmUnE+KIi2HOZrCaiyuX5wEHn3KHgC24EbgT2hLRxQGrw6zSgMozXExERERGJqJ7NuENH3N451si7tS34A95ASVx0FEvmpvDepVm9AW7p3FRmq7qkjINwAl0eUBZyXA4MLKPzIPBbM/sskAS8b7gnM7O7gLsACgsLw+iWiIiIiEj42jr97K/uG3HrWfPW0NbV2yYvPYFlOSlcvXxub3Cbn5GEL0obccvECCfQDfUpHTh/8zbgMefcP5vZRcCPzGylcy4w6IHOPQI8At6UyzD6JSIiIiIyYs45yk+29Qa2njVvh+ta6FmdlBjrY+ncFK5dncOyuSkszUllcXYKaQkxke28zHjhBLpyoCDkOJ/BUyo/CWwAcM69ambxQAZQE8brioiIiIiMSlN7F/urm/qNuO071kRzRzcAZjBvdiJL56Zyw5pcls5NZXlOKvmzEojSqJtMQuEEum3AIjObD1QAtwK3D2hTClwJPGZmy4B4oDaM1xQREREROa3QDbnfqWpkb3DkrexE34bcqfHRLM1J5YPn5rE0uDXA4uwUkuK0s5dMHaP+tDrnus3sHuB5vC0JHnXO7Tazh4DtzrnNwJeA/zKzL+BNx7zTjbaspoiIiIjMGE/vqBhxGf761s6+4DbEhtxRBgsykzk7P51b1xaydG4Ky3JSyUmLH/HWACKT1ai3LRhP2rZAREREZOZ6ekfFkBtlf/OmFazKT+9b6xa8rWoYvCH30rmpvcFtuA25RSazidi2QERERERkVAIBR3u3n9ZOP22dftq6vNvWTj//sGVPvzAH0Nbl50s/L+k9jvEZZ2Umc+GCOSwNFilZNjeFzJSRb8gtMh0o0ImIiIhMgDOZQjgZ+AOO1s7u3qDV1hUSvjr9tHb5ae/0B9sEaAu2DQ1orSFBrS+wee16pkOeqf//I2tYmpPCgoxkYqO1IbeIAp2IiIjIOBs4hbCivo37n9wFMOpQ19kdGBC2uvsFqfauoUe/2rq6Q74e6n7v607/mQUuM29aZEKMj4RY7zYx1vs6IzmWxNho4kPO9bRLHPB1fIyPzz3xFrXNHYNeIy89YVKHYJFIUKATERERGWfffX7fkFMIH9y8m+PNHb0jXqGjX23Dha/g192BM6uDEGWQGBs9KGwlxPhIT4zpC1sxPhJiowe1SYz1ER/rI3FA+EqMjSYx1kdcdNSYTXX8+2uXDbmG7r71S8bk+UWmEwU6ERERkTHW5Q9woLqZ3ZUN7K5spKK+bch29W1dfPPZvYC3JqxvpKpvNCspLpo5yXH9RrL6wlZf8Bpu9CsxJpr42ChifWMXuMZbzyjcVJqiKhIpCnQiIiIiYWjp6OadY43srmxkd0Uju6sa2H+suXfKYkKMjxif0eUfPKI2NzWe337xsmAbrQcLddM5eQpwIiOgQCciIiIyQidaOntH3bx/DRw+3kLPLlCzEmNYkZvGx9cVsTw3lRW5aczPSOKZnZVDTiH8yjVLSY2PidC7EZHpQIFOREREZADnHOUn29hd2ciekAB3rLFvv7O89ASW56Zyw9m5rMhNY0Xu8BtVawqhiIwXBToRERGZ0br9AQ4db/FG3iq84LanqpGGti7AKyayIDOZCxbMZkVw1G15TiqzkmLP6HU0hVBExoMCnYiIiMwY7V1+3jnW1G/a5L5jjb17osVGR7FsbgrvX5UTDG+pLJ2bSkKsL8I9FxEZmgKdiIiITEsNrV3srmpgT8h6t3drW/AHy/2nxEezIjeVj14wr3fk7azMJKJVnEREphAFOhEREZnSnHNUN3aEjLp5t+Un+7YKyE6NY0VuGutXzO0Nb/mzEqZMGX8RkeEo0ImIiMiUEQg4jtS19KsyuaeykbqWzt428zOSOLsgndsvKOwtVpKRHBfBXouIjB8FOhEREZmUOrr9/Tbn3l3ZyN6qRlo7vdL/MT5jcXYK712a5Y265aWxLCeV5Dj9eiMiM4e+44mIiEjENbV3sbeqf7GSgzVNvZtxJ8X6WJ6byi3FBcH93VJZlJVCbLTWu4nIzKZAJyIiIhOqtqlvvdue4LTJI3WtvfdnJMeyPDeNK5Zk9q53mzc7kagorXcTERlIgU5ERERG5ekdFafcKNs5R9mJtkHFSmqaOnrbFMxOYEVOGh88N58VeV54y0qJU7ESEZERUqATERGRM/b0jgruf3IXbV3eeraK+jb+7pclbD1cR3xMtFespKqRpvZuAHxRxsLMZC5ZmBGcMpnG8txU0hJiIvk2RESmPAU6EREROWPffX5fb5jr0dEd4Kdby4iPiWLp3FRuODu3t8rkkrkpxMdoc24RkbGmQCciIiJnpKS8nor6tiHvM2D3Nzbg03o3EZEJEVagM7MNwPcAH/AD59zDA+7/F+A9wcNEIMs5lx7Oa4qIiMjE6/IHeO7tY/zw5cO8WVqPAW6IdrnpCQpzIjI1lGyCFx6ChnJIy4crH4DVt0S6V2ds1IHOzHzA94GrgHJgm5ltds7t6WnjnPtCSPvPAueE0VcRERGZYHXNHWzcVsaPXj3KscZ25s1J5IHrlpMYG8U3ntnbb9plQoyP+9YviWBvRURGqGQTPHMvdAVnGzSUeccw5UJdOCN05wMHnXOHAMxsI3AjsGeY9rcBXw/j9URERGSC7K5s4LGXj/CrnZV0dge4dFEG3/rASt6zJKt3+4D4mOhTVrkUEZl0ujuhYjs8+6W+MNejq80bsZtBgS4PKAs5LgcuGKqhmc0D5gN/GO7JzOwu4C6AwsLCMLolIiIio9HtD/C7PdX88JUjbD18goQYHx8+L587Ly5iUXbKoPY3nZOnACcik1sgANVvw6EX4fBLcPRV6GoZvn1D+YR1bayEE+iGmiA/1HR6gFuBXzjn/MPcj3PuEeARgOLi4uGeR0RERMZYfWtn77TKivo28tIT+Or7l/KR4kLSErWtgIhMIc7BiUNeeDv0Ehz5M7TWefdlLIY1t8OCy+E3fweNFYMfn5Y/sf0dA+EEunKgIOQ4H6gcpu2twGfCeC0REREZY/urm/jhy0d4akc57V0BLlwwmweuX877lmWrsImITB1N1XD4T3D4RTj0J2go9c6n5MKiq2H+5V6IS83te0xXW/81dAAxCV5hlCkmnEC3DVhkZvOBCrzQdvvARma2BJgFvBrGa4mIiMgY8Accf3inhsdeOczLB+uIi47ipjV53LmuiGU5qZHunojI6bU3wtGXvRG4wy9BTbCER3waFF0K6+71QlzGIrBh/jjVs05uJle5dM51m9k9wPN42xY86pzbbWYPAdudc5uDTW8DNjrnNI1SREQkQhrbu9i0rYz/fvUopSdayUmL5283LOHWtYXMToqNdPdERIbX3QFlW4PTKF+EijfB+SE6Hgov9ELY/Msh52yI8o38eVffMiUD3EA2GXNWcXGx2759e6S7ISIiMuW9W9vM468c4RdvlNPa6ad43iw+vm4+V6/IJsYXFenuiYgMFvDDsRIvvB16CUpfg+42sCjIO69vCmX++RATH+nejhsze8M5V3y6dmFtLC4iIiKTTyDgeOlALY+9fISX9tcS64vi+rNzufPiIlblp0W6eyIi/TkHde/CoT96o3CH/wzt9d59mcvgvDu8EFe0zptWKf0o0ImIiEwTzR3d/PKNch5/5QiHjreQmRLHF69azG3nF5KZEhfp7omI9Gms6qtEefilvoqTaQWw9DpvBG7+ZZAyN7L9nAIU6ERERKa4o3UtPP7KUX6+vYymjm7OLkjne7eu4ZqVOcRGa1qliEwCbfVw5C99Ie74Pu98wiwvuM3/Eiy4AmYvGL6QiQxJgU5ERGQKcs7x8sE6HnvlMC+8U4PPjGtX53DnxUWcUzgr0t0TkZmuqw3KXu9bB1f1FrgAxCRC4UVwzse8UbjsVRClPzyFQ4FORERkCmnt7OapHRU89vIRDtQ0Mycpls++ZyEfvXAe2anTtziAiExyAT9UvtW3Dq70dfB3gPkgvxguu89bB5e/FqJVWXcsKdCJiIhMAeUnW/nRq0fZuK2MhrYuVuSm8k8fPpvrVucQH3MGZbpFRMaCc1C7r28K5ZG/QEeDd1/2Slj7KW8Ebt7FEJcS2b5Ocwp0IiIik5RzjtcPn+Cxl4/w2z3HMDM2rJjLneuKKJ43C9M6ExGZSA3lfUVMDr0Ezce88+nzYMWN3gjc/MshOTOy/ZxhFOhEREQmmfYuP5vfquSHrxxhb1Uj6Ykx3H35WXzswnnkpSdEunsiMlO0noAjf/bC26EX4cS73vnEDK+QyYJggJs9P6LdnOkU6ERERCaJqoY2fvzaUX76eiknW7tYkp3Cwzev4qZz8jStUkTGX2crlL4aHIF7EapKAAcxSd4ecMWf8CpRZi1XIZNJRIFOREQkgpxzvFl6kkdfPsJzbx8j4BxXLcvmznVFXLRgjqZVisj48XdD5Zt9lSjLt4K/E6JivOIlV9zvjcLlnQe+mEj3VoahQCciIhIBHd1+ni2p4ocvH2FXRQMp8dF8Yl0Rf31REQWzEyPdPRGZjpyDmr1egDv8Ehx5GTqbAIO5q+CCu2H+FTDvIohNinBnZaQU6ERERCZQTVM7P36tlJ++fpTjzZ2clZnEP9y0kpvPySMpTj+WRWSUSjbBCw95hUvS8uHKB2D1LXDyaF8Rk8N/gpYar/3sBbDqQ94IXNFlkDQnsv2XUdNPDhERkQmws6yeH758mGd3VdHld7x3aRYfX1fEJQszNK1SRMJTsgmeudfbzBugoQye+jQ8dz+0HvfOJWX1FTFZcDmkF0auvzKmFOhERETGSZc/wG/ePsYPXz7MjtJ6kuOi+egF87jj4iLmZ2g6k4iEoasdju/zplD++st9Ya6H80NnM2z4thfgMpeC/ng0LSnQiYiIjLG65g6e2FrKj147SnVjB0VzEvn69cv50Hn5pMSrsICInAF/N5w4BDV7vPDWc3viXXCBUz+2uwMu/PTE9FMiRoFORERkjLxd0cBjrxxh885KOrsDXLoog4dvXs3lizOJitJfxkXkFJzzpkqGhraaPVC7H/wdwUbmrX3LWgYrPuDdZq+AH9/srZ0bKC1/Qt+CRIYCnYiISBi6/QF+u6eax14+wtYjJ0iI8XFLcT53XlzEwqyUSHdPRCaj5trBI241e4MVJ4NS87zAtuAKyFrhfZ2xGGKHqIJ75df7r6EDiEnwCqPItKdAJyIiMgr1rZ08sbWMH716hMqGdvJnJfD371/GLWsLSEvQtEoRAdobofadweGtpbavTcIsL7Ctuc0LbVnLvfVuCekjf53Vt3i3Q1W5lGlPgU5EROQM7DvWxGOvHOapHRW0dwW4aMEcHrxhBVcuy8anaZUiM1NXOxzfP3jEraG0r01MEmQthcUbvNDWE96Ss8amWMnqWxTgZqiwAp2ZbQC+B/iAHzjnHh6izS3Ag4ADdjrnbg/nNUVERCaaP+B4YW81j71yhFferSMuOoqbz83jjouLWDo3NdLdE5GJ4u+Gk4f7j7hV7+lfoCQqxpsaWXgBZN3ZF97SCiEqKqLdl+lp1IHOzHzA94GrgHJgm5ltds7tCWmzCLgfWOecO2lmWeF2WEREZLw8vaOC7z6/j8r6NnLTE/jMe8+itcPP468eoexEG7lp8fzdhqXcuraAWUmxke6uiIwX57ypi/1G3HafvkBJ1nKYcxb4NO1aJk44I3TnAwedc4cAzGwjcCOwJ6TN/wC+75w7CeCcqwnj9URERMbN0zsquP/JXbR1+QGoqG/jq0++DcD5RbO5/5plXL08m2if/sIuMq20HB+6QElHY1+bfgVKgiNuGUuGLlAiMsHCCXR5QFnIcTlwwYA2iwHM7GW8aZkPOueeC+M1RURERq21s5vjTZ3UNndwvOdfUyfHmzv4+RtltHcN3tMpMyWOTZ++KAK9FZEx1dEENQMLlOwZukDJ6o/0jbhlLTuzAiUiEyycQDfU6k03xPMvAq4A8oE/m9lK51z9oCczuwu4C6CwsDCMbomIyEzhnKOl08/xpr6AVtvc2f+4qYPjzV5oa+30D/k8aQkxQ4Y5gONNHUOeF5FJqrtjcIGS6j3DFChZH1KgZMXYFSgRmUDhBLpyoCDkOB+oHKLNa865LuCwme3DC3jbBj6Zc+4R4BGA4uLigcFQRERmCOccje3dwdGzvjDWF9D6Hw8XxGYnxZKRHEtGchxrCtLJSI4jI8U7zkyO6z2ekxRHbHQU6x7+AxX1bYOeJzc9YbzfsogMpWTTqcvwB/xw4vDgEbe6d8EF/3jTU6Ck4Hw47w5vE24VKJFpJpxAtw1YZGbzgQrgVmBgBcungduAx8wsA28K5qEwXlNERKYg5xwNbV1DBrLj/Y696ZCd3YNDWpT1hDQvjBXNSQyGsmA4S44lM8ULa7OTYs94rdt965f0W0MHkBDj4771S8J+/yJyhko29d8ou6EMfvUZ2P+cF9Jq9nijcN3twQcYzJ7vjbYtvzE46qYCJTIzjDrQOee6zewe4Hm89XGPOud2m9lDwHbn3ObgfVeb2R7AD9znnKsbi46LiEhkBQKOk62d/UbQaocYUTve1EldSwdd/sGTL3xRxpyekJYSx1lZyf1Gz3rCW0YwpI3nPm83nZMH0K/K5X3rl/SeF5Fx5hw0V3ujbb++ry/M9fB3wtu/hJTcYIGSy1WgRAQw5ybf7Mbi4mK3ffv2SHdDRGTKG1iG/3QBxR9wnGgZegStNjiC1rM+ra6lE39g8M+QGJ8xJ2lwIAsdQesZVUtPiCFKm3GLzDzNtVC71ytS0nNbswfaB5VZGMDgwdO1EZkezOwN51zx6dqFtbG4iIhMXkOV4f/bX5Tw+uE65s1JCikc0hfgTrR0MkRGIzY6KjhyFktOWjyr8tIGBbbM4HFaQgymogIiAtB6whtx6w1v73jHrcf72sSn99/LLXMpPP1paBxYmgFvLZ2I9KNAJyIyTf3jb/b2Ww8G0OkP8MRWb8eZ+Jio3jCWPyuRcwrTB42oZaTEkZkSR0pctEKaiAyvvaFvlK0ntNW+402h7BGb4gW2pe+HzGVelcnMZZAyd3Blyfd9o/8aOoCYBK8wioj0o0AnIjKNdHT7eX53NT/bVkp149Dl9g3Y9Y31JMX6FNJE5Mx0NEHtvr7Nt3tG3ppCRtNikiBzCSx8X3DELRjeUvNGviVATzXLU1W5FBFAgU5EZFo4WNPEE1vLePJQlyW3AAAgAElEQVTNck62dpGXnkBKfDRN7d2D2uamJ5Acp2//InIKnS1ecOsZbesZcWso62sTnQCZi2H+ZV5gy1ruTZdMKxibLQFW36IAJzIC+okuIjJFtXX6eXZXFRu3lrL96Emio4yrV2Rz69pCLlmYweadlSrDLyKn1tUW3IR7QHGS+lIguKDWF+tVkSy8EDLv7FvnNqsIonwR7LyIgAKdiMiU83ZFAxu3lfKrHZU0dXSzICOJ+69ZygfPyycjOa63ncrwi0iv7g6oO9h/tK1mL5w8DC6472NUDMxZCHnnwTkf80Jb1jKYNR98+pVRZLLS/50iIlNAU3sXv3qrko3bSnm7opHY6CiuXZXDR9YWcMH82cOuhbvpnDwFOJGZxN8Fde8GR9tCwlvdu+CCo/Xm8zbczl4Bqz7cV5xEm3CLTEkKdCIik5RzjjdL69m4tZQtJVW0dflZOjeFB69fzgfOySctUb94icxY/m5vdK13tG2PN12y7iAEurw2FuWNrmUtg2U3eLdZy7xRuOi4Uz+/iEwZCnQiIpPMyZZOntxRwc+2lbK/upnEWB83rsnlI2sLWFOQrsqUIjNJwA8nj/TfCqBmLxw/AP6QSrazirxRtiUb+qpKZiz2Sv2LyLSmQCciMgk453j1UB0bt5bx3O5jdHYHODs/jX+8eRXXn52rqpQi00HJpuHL8AcC0FDavzhJ7V6o3Q/dIXuxpRV4a9vOem9fcZLMJRCbFJn3JCIRp98QREQiqKapnV++4Y3GHalrJTU+mtvWFvCRtYUsz02NdPdEZKyUbOq/UXZDGTz9/8H2H0J3u7dFQFdLX/uUXG+UrfgTIVsCLIG4lMj0X0QmLQU6EZEJ5g84/nSglo1bS3lhbw3dAcf5RbO598pFvH9VDvExKgMuMi0EAnDiEFS9Bc9+sS/M9d7fBWWvQdGlcO5f9VWVzFwKCemR6bOITDkKdCIiE6Sivo2fby/j59vLqahvY3ZSLJ+4ZD63FBewMCs50t0TkXD4u7393Kp29v07VgKdzad+nHNwx+aJ6aOITEsKdCIi46jLH+CFvTVs3FbKS/trcQ4uXZTBV9+/jKuWZxMbHRXpLorImeru8KpKVu2EqhLvtvptb+okQEwizF0FZ98GOWd7/zbe5q2dGygtf2L7LiLTjgKdiMg4OHK8hY3byvjFG+Ucb+4gOzWOe96zkFuKCyiYnRjp7onISHW2QPXuYHh7y7ut2QuBbu/+uFQvsK39VF94m7MQogZMnb7y6/3X0IFXgfLKBybuvYjItKRAJyIyRtq7/Dy/+xgbt5bx6qE6ogzeuzSLW9cWcsWSTKJ9Go0TmdTaG+DYrv7TJo/vBxfw7k+c4wW2i9/XF97SiyBqBP9v91SzHK7KpYjIKCnQiYiEaX91Exu3lvHkjnLqW7vIn5XAl65azIeLC5ibFh/p7onIUFrq+kbceta7nTjUd39KrhfYlt/UF95ScyGcfSBX36IAJyJjToFORGQUWju72VJSxcatpbxZWk+Mz7h6+VxuPb+AdWdlEBWlzb9FJgXnoOlY/1G3qp3QGLKeLX2eF9jWfBRy1kDOakjOilyfRUTOgAKdiMgZ2FXewMZtpWx+q5Kmjm4WZCbx9+9fxs3n5jEnOS7S3ROZ2ZyD+qMhwS1YsKSlJtjAIGMRzLuob9Rt7ipImBXRbouIhEOBTkTkNBrbu/jVW5Vs3FrK7spG4qKjuHZVDreeX8jaollYOFOwRGR0AgE48W7/YiVVO711cADm8/Z0W3RVX3jLXglx2iJERKaXsAKdmW0Avgf4gB845x4ecP+dwHeBiuCpf3fO/SCc1xQRmQjOOd44epKN28p4tqSKti4/y3JSeejGFdx4dh5piTGR7qLIzOHvhuP7BuzxtqtvjzdfHGQvhxUf6AtvWSsgRmtYRWT6G3WgMzMf8H3gKqAc2GZmm51zewY0/Zlz7p4w+igiMmFOtHTy5Jvl/GxbGQdqmkmK9XHTObncuraQ1flpGo0TGW9d7SF7vAX/1ewJ2eMtyZsmueajfeEtcwn49EcWEZmZwhmhOx846Jw7BGBmG4EbgYGBTkRkUgsEHK8equOJraX8dnc1nf4AawrSefjmVVx/di5JcZqdLjIuOlvg2Nv9w1tt6B5vaV6BkrWfChYrORvmnDV4jzcRkRksnN9S8oCykONy4IIh2n3QzC4D9gNfcM6VDdEGM7sLuAugsLAwjG6JiIxMTWM7P3+jnE3byzha10pqfDS3X1DIR9YWsCwnNdLdE5le2uqH3uMN592fmAG5a2Dx1SF7vM0Lb5sAEZEZIJxAN9R3WDfg+BngCedch5l9GngceO9QT+acewR4BKC4uHjg84iIjAl/wPHS/ho2bi3jhXdq8Acc58+fzefft4hrVuYQH6O//IuMWMmmoTfKbjnev1BJ1U44eaTvcal5XmBbeXNfeEvJUXgTERmFcAJdOVAQcpwPVIY2cM7VhRz+F/DtMF5PRGTUyk+2sml7OT/fXkZVQztzkmL51CXz+cjaAhZkquqdyBkr2QTP3Atdbd5xQxk8dTf8+j5or+9rN2u+N13y3Du86ZNzz4bkzMj0WURkGgon0G0DFpnZfLwqlrcCt4c2MLMc51xV8PAGYG8Yrycicka6/AFe2FvNE1vL+NOBWgAuXZTJA9ct58pl2cRGR0W4hyKTnHPQXOPt7XbyCJzsuT0Cpa+C8w9oHwB/J1z9rZA93tIj0HERkZlj1IHOOddtZvcAz+NtW/Coc263mT0EbHfObQbuNbMbgG7gBHDnGPRZROSUDh9vYeO2Un75RjnHmzuZmxrPZ9+zkA8XF1AwOzHS3ROZXDpb+ge10PBWfxS6Wvu3T8nx1rYNDHM9utrgYhW3FhGZKObc5FuuVlxc7LZv3x7pbojIFNLe5ee5t4/xxNZSXj98Al+U8d6lWdy6toDLF2cS7dNonMxQ/m5orBh6lK3+KLTU9m8fmwyzivr+pc8Lfj0P0gshJsFr9y8rvWmWA6UVwBfeHsc3JCIyM5jZG8654tO1Uy1uEZlSnt5RwXef30dlfRu56Ql87MJCqhs7eGpHBQ1tXRTMTuC+9Uv40Hn5ZKdqU2GZAZyDtpN9IW3gKFtDWd82AADm8wqYzCqCJe/3gtqsIkgv8m4TZ4+sOMmVD/RfQwde2LvygbF7byIicloKdCIyZTy9o4L7n9xFW5c31auivo1vP7ePKIP3r8rh1rWFXHzWHKKiVClPppmudqgvDQlqR/pPi+xo7N8+McMLannnwooPhIy4zYPUfPCNwY//1bd4t0NVuRQRkQmjQCciU0J7l5+HtuzpDXOhslPj+ffbz41Ar0TGSCAAzceGX8vWVNm/fXR831TIeReHjLLN876OS5mYfq++RQFORCTCFOhEZNLq7A7wl4O1bNlZxW/3VNPc0T1ku2MN7RPcM5FRaG8cHNR6w1sp+DtCGhuk5noh7az39F/HNqsIkrO1Z5uIiAAKdCIyyXT7A7x26ATP7Kzkud3HaGjrIjU+mmtWzuUP79RQ19I56DG56QkR6KnIAP4ub73acKNsbSf6t49L8wJa1jJYsqH/Orb0AoiOm+h3ICIiU5ACnYhEXCDg2HbkBM+UVPKbXceoa+kkKdbHVcuzuW51LpcuziAu2jdoDR1AQoyP+9YviWDvZVop2TT8mjDnoOV4SFA7HBLejkJjubcPW4+oGC+YzSryNtYOXcc2qwgSZk30uxMRkWlIgU5EIsI5x46yerbsrOLZXZVUN3YQHxPFlUuzuf7sHK5YkkV8jK/fY246Jw+gX5XL+9Yv6T0vEpaSTbD5XugOVm1sKIOn/wZe+XcIdHmhraul/2OSsrxwVnjhgHVsRd6UySgfIiIi40n70InIhHHOsbuykWd2VrKlpIqK+jZifVFcviST61bn8L5l2STF6e9MMs7aG73RtROH4ETw9uQROPrK0JtlR0XDoqtD1rEV9e3JFps0wZ0XEZGZQvvQicikse9YUzDEVXKkrpXoKOOSRRl84arFXL0im9T4mEh3UaYT56C1bkBgCwlwrcf7t0/KhNkLhg5zAAE/3PbE+PdbRERkFBToRGRcHKptZktJFc/srORATTNRBhedNYe7Lz+LDSvmMispNtJdlKksEICmqmBIGxDYThyGzqaQxta3kfbSa2H2fC/AzZrvfd1T4v9fVnrTLAdKy5+IdyQiIjIqCnQiMmbKTrT2hrg9Vd5Gx+cXzeahG1dwzcocMlNUtU/OgL/LK+d/8nBfUOsNb4f7l/mPivamRM5e4K1n6w1sC7ypkTHxp3+9Kx+AZ+6Frra+czEJ3nkREZFJSoFORMJS1dDGsyVVbCmp4q2yegDWFKTzP69dxrWrc8hJ05YCcgpdbd76taGmR9aX9Z8GGZ3gjajNWQiLruoLbLPnQ2o++ML8kdZTzXK4KpciIiKTkIqiiMgZq23q4DdvV7FlZxVbj3h7a63ITeW61blctzqHgtmJEe6hTCrtDUMEtuC/psr+bePT+o+uhU6PTJmrzbRFRGTGUFEUERlTJ1s6eW73MbaUVPLqu3UEHCzKSuaLVy3mutU5LMhMjnQXJVJ69mcbtJYtuL5t4IbaydleQFtwxeD1bImzI/EOREREpiwFOhEZVmN7F7/dXc2Wkkr+cuA43QFH0ZxEPvOehVy3Opclc1Mi3UWZKIEANFYMUe6/pwhJc0hjg7QCL6Atv6H/iNusIohT+BcRERkrCnQi0k9LRze/31vNlpIqXtpXS6c/QF56Ap+8dD7Xr85lRW4qpmlvU0fJppGvCevu9Ko8DrWe7eTRAUVIYrxwNns+zFvXf4pkeiFEqwCOiIjIRFCgExHau/z88Z0atpRU8cI71bR3BchOjeNjF87jurNzOKcgXSFuKirZ1L9qY0MZbL7XC3cZiwdPkWwoAxfoe3xMohfSMhbD4g39p0em5UOULzLvS0RERHop0InMUB3dfv68/zhbSir53Z5qWjr9zEmK5cPnFXDd6hzWFs0mKkohbkoK+L3Kkc/d378EP0B3G7zwjb7jhFleQMtf643chU6PTM5SERIREZFJToFOZAbp9gd45d06ntlZyfO7j9HY3k1aQgzXn53LdatzuXDBbKJ9UZHupoxUZwscPxD8tx+O7/O+rjsI/s5TP/Z//NEbcUuYNTF9FRERkXERVqAzsw3A9wAf8APn3MPDtPsQ8HNgrXNO+xGITCB/wLH18AmeKankubePcaKlk+S4aK5ens31Z+eybmEGsdEKcZOWc9BSC7X7gqHtQF9wayjra2dR3shaxmJY+D7IXAK//wa01Ax+zrQCyDt34t6DiIiIjJtRBzoz8wHfB64CyoFtZrbZObdnQLsU4F7g9XA6KiIjFwg4dpSd5JmdVTy7q4rapg4SYnxcuSyL68/O5fLFmcTHaP3TpOLvhvqjQwS3/d4+bj1ikiBjERReBBl3QOZiL8TNXjC4EIkvtv8aOoCYBK8wioiIiEwL4YzQnQ8cdM4dAjCzjcCNwJ4B7f4B+A7w5TBeS0ROwznHrooGtpRUsWVnJZUN7cRGR/GeJZlctzqXK5dlkRirWdYR19EMdQegdn8wuAX/1b0Lga6+dsnZXlBb+SHvtie4peRC1AhHVHuqWY60yqWIiIhMOeH8dpcHhMz3oRy4ILSBmZ0DFDjntpiZAp3IGHPO8c6xJraUVLKlpIqjda3E+IxLF2Xy5fVLuGp5NinxMZHu5szjHDRXe0Gtdl/IGrf93l5uPcznrWPLWOJVkcxY7E2VnLMQEtLHpi+rb1GAExERmcbCCXRDlT5zvXeaRQH/Atw5oiczuwu4C6CwsDCMbolMfwdrmntD3MGaZnxRxsVnzeEzVyxk/Yq5pCUqxE0If5dXTXJQcDsAHSHTJGOTvWmSRZd6tz3BbdZ8iI6NWPdFRERk6gsn0JUDBSHH+UBlyHEKsBJ4Mbh/1Vxgs5ndMFRhFOfcI8AjAMXFxW7g/SIzXWldK88EQ9zeqkbM4Pyi2dxx00quWTmXjGRt5Dxu2hu9aZLHD/Rf43biUP9pkik5XmBbfYsX2jIWecEtJUfl/0VERGRchBPotgGLzGw+UAHcCtzec6dzrgHI6Dk2sxeBL6vKpcjIVda38WxJFc+UVFJS7o34nFuYzgPXLefa1Tlkp8ZHuIfTiHPQVNUX1kKDW1PI36qiovs22156bTC4BcNbfGrk+i8iIiIz0qgDnXOu28zuAZ7H27bgUefcbjN7CNjunNs8Vp0Umc6e3lHBd5/fR2V9G7npCdx9+XwCAdhSUsX2oycBWJWXxv3XLOXa1Tnkz0qMcI+nuO5OOHl46GmSnU197eJSvZC24PKQ0LbYW/Pm05RWERERmRzMuck3u7G4uNht366BPJn+nt5Rwf1P7qKtyz/ovqVzU7hudQ7Xrc6lKCMpAr2bpEo2jaxqY3tDX1gLDW4nD0Ogu69dal5wXduSvvVtGYshZa6mSYqIiEjEmNkbzrni07VTDXORCdbtD7CvuokdpfV869n/x959x1dZ3v8ff10ZJAGyIMwMAoLsyJ6CAxVcSBWsomi1jmqt1ra22v5UtLWlYlvrt0Ot1TpABUXEiXVVUQEDwcgGgUwgEMgi+5zr98d9AieLhKyT8X4+Hnmcc+77Ovf9OTEteeda22oMcz1Dg3j/p9N9UF0rl7ys8r5quWmw6idwcAuE9a08TLLgwIn3+QVC99Og5xAYdtmJIZJRgyAo1DefRURERKQJKNCJNLPsghKSUnPYmHqUpNQcvknPobC0eojzdii/pIWqa0Oshf/eX3mTbIDyYvjiced5ULizX9vAGZWHSUbGg7/+705ERETaH/2GI9KEyl1uth/IPx7eNqYeJSW7EIAAP8PQPmHMGxvD6LhIxsRFcvW/viIjp7jadfpGhLR06a2PqxwOJEPqWkj90nk8dqiWxgZ+vgO69tQwSREREelQFOhEGuFQfglJqUfZ6Alv36bnHh9C2SM0iDFxEVw9IY4xcZGMjA4npJN/pfffM3NItTl0IYH+3DNzcIt+jlah9BikJ54IcGlfQ9kx51xEPzhtBuxaDUVHq783PAZCe7VsvSIiIiKtgAKdSD2Vudxs25/HxpSjJKU5AS7tiDP8L8DPMLxvGN8fH8uYfpGMjo0gJjIEU0dv0ZzR0QCVVrm8Z+bg48fbtWPZkPrVia/933gWKzHQawSMmg/9JkPcZGd+HFSfQwcQGOIsjCIiIiLSAWmVS5FaZOUVVxo6mZyeS0m5G4BeYUGMiYtkdFwEY+IiGREdTnCgfx1X7MCshaP7PL1vngB3eKdzzj8IosdC3CToNwVixkNIRO3Xqu8qlyIiIiJtmFa5FDkFpeVutnp63ypCXEaO0wvUyd+P4dFhXDOxH2P6OQGuT3hwnb1vHZrb5aw86T3/LX+/cy44HGInwRlXOwGu72gICKr/tROuVIATERER8VCgkw7pQG5F75sz/+3bjFxKPb1vfcKDGRMXyQ1T4xkdF8nwvmHqfatLWTFkbDjR+5a2HkrynHNh0dBv6okeuB5Dwc/Pt/WKiIiItBMKdNLulZS72JLpmfuWmkNS6lEyc52VJTsF+DEyOpzrJvVz5r7FRdAnXCtM1qnoKKSuOxHgMpPAVeqc6zEURlzhhLe4SRAR59taRURERNoxBTppdzJziirNfduSkUepy+l9i44IYUy/SG6Ki2RMv0iG9gklKEC9b3XKTYcUrwVMsrY6x/0Coe8omPgjZ/GSuEnQuZtvaxURERHpQBTopE0rLnOxOSO30sbdB/Kc3regAD8SYsI9QycjGB0XSa+wYB9X3Aa43XB4B6R8eWIRk9w051ynUIidAMMvd1ag7DsGOnX2bb0iIiIiHZgCnbQZ1loycoqcPd88WwdszcylzOWs1BrbLYQJ/bsxJi6CMf0iGdI7jE4BmqtVp/JSZ8jk8S0E1kJxjnOuay+n523yHU6A6zkc/PV/GyIiIiKthX4zk1aruMxFcnquZ+ESZ/GSQ/klAAQH+pEQE8EPzxzAmLgIRsVF0DNUvW/1UpznLFpSEeAyNkC506tJ94Ew9FInxPWbDJH9Qat5ioiIiLRaCnTSKlhrSTtSRFLaUa/etzzK3U7vW7/unTlzYNTxfd8G9w4l0F+9b/WSf8Br+OSXznYC1g3GH/okwLgfOnPf4iZD1x6+rlZEREREToECnTS5lUkZLF69g8ycIvpGhHDPzMHMGR1dqU1haTnJ6bnH570lpR7lcIGzSmLnTv4kxIRzy/QBjImLZFRcBFFdT2Gfso7MWsjeXTnAHd3nnAvsDDHjYPovnQAXMx6Cuvq0XBERERFpHAU6aVIrkzK4b8W3FJW5AMjIKeK+Fckcyi8hKrQTG1OcxUu2H8jH5el96x/Vhemn92BMnLNtwOBeoQSo961+XGVwINlrBcq1UHjYOde5u9PrNv5m57FPAvgH+rZeEREREWlSCnTSpBav3nE8zFUoKnPzyLvbAOjSyZ9RcRHcfvZpzsqTsZFEdunki1LbppICyEg8EeDSv4ayQudcZDwMOt+zfcBkiBqk+W8iIiIi7ZwCnTSpzJyiWs+9/9NpDOoZir+fQkYlycvgo4edvd7CY2DGA5BwpXOu4BCkrT0R4PZ/A9YFGOg9AkZfe2L/t7C+Pv0YIiIiItLyFOikyeQVlxEU4EdxubvaueiIEIb0DvNBVa1c8jJ4604o8wTh3DR488ew4T9QcNCZDwfgH+TMfzvzpxA3BWLHQ3C4z8oWERERkdZBgU6axHeHCrj5hURKyt0E+pvje8MBhAT6c8/MwT6srhUqynHC2nu/OhHmKrhKnUVNTp/p6YGbAn1HQYAWhhERERGRyhoV6Iwxs4C/Av7AM9baRVXO/wj4MeACCoBbrLVbG3NPaX0+2Z7FnS8nERjgx8u3TOJAbnGdq1x2CK5yyElxgtvhnXB414nnxw7V/f75rzZ/jSIiIiLSpjU40Blj/IG/A+cD6cDXxphVVQLbUmvtk572s4E/A7MaUa+0ItZa/vm/71i8egdDe4fx9HVjiYnsDNCxAlzRUTi8G7J3OaHt8E4nuB3Z4/S2VQjpBlGnOz1v3Qc5z9+529knrqrwmJarX0RERETarMb00E0Adltr9wAYY14BLgOOBzprbZ5X+y6ARdqFolIX97z2DW8n7+eShD4snnsGIZ38fV1W86lvb5tfAET2d1aYPB7cBjmPXbpXv25pQeU5dACBIc7CKCIiIiIidWhMoIsG0rxepwMTqzYyxvwY+BnQCTi3tosZY24BbgGIi4trRFnS3NKPFnLLCxvYdiCPX80awo/OGoBpL8vjN6S3Ler0E8EtMv7U9nqrWM2ytlUuRUREREROwljbsE4zY8w8YKa19ibP6wXABGvtT2ppP9/T/vq6rj1u3DibmJjYoLqkea3dk83tSzZS5nLzxFWjOWdIT1+XdOpq6m07vMsJcTX2tp0OUQNPDJOMGgSdu/mufhERERFp94wxG6y14+pq15geunQg1ut1DJB5kvavAP9sxP3Eh6y1vLg2hYff2kq/7p3513XjGNCjq6/LOrlKvW1ewySzvwN32Yl2nbs7Ya2xvW0iIiIiIi2sMYHua2CQMaY/kAFcBcz3bmCMGWSt3eV5eTGwC2lzSspdPLByC68mpjFjSE/+ctUowoJbSdCp6G2r6GGrT29b1eCm3jYRERERaaMaHOisteXGmDuA1TjbFjxrrd1ijHkYSLTWrgLuMMacB5QBR4E6h1tK65KVV8yPXtrAxtQc7jhnID87/3T8/HwwX+6Ue9tmnViMJOp0iOyn3jYRERERaXcaPIeuOWkOXeuwKS2HW19MJK+onMfmncHFCX2a94an0tvWbYAnrA1Ub5uIiIiItDstMYdO2rHXN6Rz3xvf0jM0iNdvm8KwvmH1f3PyspOv2lhTb9vhXc5KkifrbasIbuptExEREREBFOikinKXm9+/u51nv9jL5AHd+fs1Y+jWpVP9L5C8rPK+arlpsPJ22PACWNfJe9sGX+g1TFK9bSIiIiIidVGgk+OOHivljpc38sXubH4wJZ7fXDyUQH+/U7vIRw9V3iQbnF631C8gdqJ620REREREmpACnQCw/UAeN7+QyMHcEh6dm8CV42LrflNVuz90hlnWxFq48f3GFSkiIiIiIpUo0Anvb97Pz5Z9Q9egAF65dRJj4iJP7QLZ38HqX8PO98HPH9yu6m3CY5qmWBEREREROU6BrgNzuy2Pf7iTJz7ezajYCJ5aMJZeYcH1v0BxLny2GNY+CQHBcP7D0KUHvPOzysMuA0OchVFERERERKRJKdB1UPnFZdz96jd8uO0g88bG8Ns5IwgO9K/fm90u2LTEWcny2GEYfQ2c+wCE9nLO+wWcfJVLERERERFpEgp0HdDew8e4+YVE9h4+xsJLh3H9lHiMqedm4Slfwfu/gv3fOIuczF8G0WMqt0m4UgFORERERKQFKNB1MP/beYifLN2Iv5/hxRsnMGVgVP3emJMG/30AtqyAsGi44t8w4gqobxAUEREREZEmp0DXQVhrefqzPfzx/e2c3iuUf103jthunet+Y2khfPFX5wsLZ90LU++ETl2avWYRERERETk5BboOoLjMxa9eT+bNTZlcPLIPi+cl0LlTHf/prYXNrzu9cnkZMPxyOP8hiIhrmaJFRERERKROCnTtXEZOEbe+mMiWzDzumTmY288+re75cplJ8N69kLYWeifAFc9AvyktU7CIiIiIiNSbAl07tn7vEW5fsoHiMjfPXDeOGUN7nfwN+Qfh44chaQl07g6XPgGjr3X2lhMRERERkVZHga6demltCgtXbSGuW2deuWUcA3t2rb1xeQmsexL+txjKi2HKHTD9HggOb7mCRURERETklCnQtTOl5W4WvrWFpetSOXtwD/561WjCQwJrbmwt7HwfVv8ajuyB02fBBY9A1MCWLVpERERERBpEgaHtRNAAACAASURBVK4dOZRfwu1LNvD1vqPcdvZp/OKCwfj71TJfLms7rL4PvvsYok6Ha16HQee1bMEiIiIiItIoCnTtRHJ6Dre+uIGjhaU8cfVoZp/Rt+aGhUfg00Xw9TMQ1BVmLYLxN4F/Lb14IiIiIiLSainQtQMrkzL41evJRHUN4rUfTWFEdA1z31zlsOE5+OQRKM6FsTfAOb+BLt1bvmAREREREWkSCnRtmMtt+eP723n6sz1M6N+Nf14zhu5dg6o33PMpvH8fZG2F+GlOr1zvES1er4iIiIiINC0FujYqt7CMO17eyOe7DnPd5H7cf8kwAv39Kjc6shc++H+w/W1nQ/ArX4Shl0Jd+9CJiIiIiEib0KhAZ4yZBfwV8AeesdYuqnL+Z8BNQDlwCLjRWpvSmHsK7DyYz80vJJKZU8Siy0dy1YS4yg1K8uHzP8FXfwe/QJjxAEz6MQQG+6ZgERERERFpFg0OdMYYf+DvwPlAOvC1MWaVtXarV7MkYJy1ttAYcxvwKPD9xhTc0X2w5QB3v7qJkE4BvHLLJMb263bipNsNya/Ahwuh4CCccTXMeBDC+visXhERERERaT6N6aGbAOy21u4BMMa8AlwGHA901tpPvNqvBa5txP06NLfb8n8f7+YvH+7kjJhwnlwwlj7hIScapK2H934FmRsheixctRRixvmuYBERERERaXaNCXTRQJrX63Rg4kna/xB4r7aTxphbgFsA4uLiamvWIRWUlPPzZZtYveUgl4+J5vffG0lwoL9zMi/T6ZFLfhW69obvPQUjrwQ/v5NeU0RERERE2r7GBLqaVtawNTY05lpgHHBWbRez1j4NPA0wbty4Gq/TEaVkH+PmFxLZnVXA/ZcM48ap8RhjoKwIvvwbrPkzuF0w7Rdw5t3O3nIiIiIiItIhNCbQpQOxXq9jgMyqjYwx5wG/Ac6y1pY04n4dzppdh/nx0o0AvHDjRM4cFAXWwpaV8MH9kJsKQ2fDBb+FyHjfFisiIiIiIi2uMYHua2CQMaY/kAFcBcz3bmCMGQ08Bcyy1mY14l4dirWWf6/Zy+/f3cagnqE8fd1Y+nXvAvuTnf3kUtZAz+Fw/VvQf7qvyxURERERER9pcKCz1pYbY+4AVuNsW/CstXaLMeZhINFauwpYDHQFlhtn77NUa+3sJqi73Souc/HrFd+yIimDWcN786crz6BLeQ689WvY+AIER8DFf4Yx14O/thEUEREREenIGpUIrLXvAu9WOfaA1/PzGnP9jmZ/bhG3vriB5PRcfnb+6dwxPQ6/xKfg0z9C2TGY+CM465cQEunrUkVEREREpBVQF08rsSHlCLe+uJGi0nKeXjCWCzp9C09eDdm74LQZMOsP0GOwr8sUEREREZFWRIGuFXh5fSoPvLmZ6IgQls+Nov+Gn8CuD6DbaTB/GQy6AExNi4qKiIiIiEhHpkDnQ2UuNw+/tZUX16Yw87RgnujzPkHLnoHAznDB72DCrRDQyddlioiIiIhIK6VA5yOHC0q4fclGEvce5p9DNjMr61+YjCMw5jo4937o2sPXJYqIiIiISCunQOcDmzNyufXFDfQvSCKp56uE79sBcVPgwkXQ5wxflyciIiIiIm2EAl0LW/VNJn997b88HPgKMwK+BGJg7nMw/HuaJyciIiIiIqdEga6FuNyWx9/dRODax3kv4F0C/fxh+q9hyk+gU2dflyciIiIiIm2QAl0LyC0s5ZV//4n5h5+mT8ARXMPnYi54CMJjfF2aiIiIiIi0YQp0zSzt28/JfePn3OreQXb4MJj7Mv5xk3xdloi0YWVlZaSnp1NcXOzrUkQaJDg4mJiYGAIDA31diohIm6dA11zyD5D5+n3E7ltBCBHsnfoo/WfcDH5+vq5MRNq49PR0QkNDiY+Px2jurbQx1lqys7NJT0+nf//+vi5HRKTNU6BramXF2LX/oOzTxUSVl/BayBVMvWER/Xv19HVlItJOFBcXK8xJm2WMoXv37hw6dMjXpYiItAsKdE3FWtj+Du7Vv8EvZx+fusayftDd/OLqiwgO9Pd1dSLSzijMSVumn18RkaajQNcUDm6F9++Fvf8jzS+W+0vvY9qsK/nNtP76R0tERERERJqNJnQ1RuEReOcX8ORUyjI2scjcyBz3o9z0gx9y8/QBCnMiItIi9u3bx4gRI5rl2p9++imXXHIJAKtWrWLRokXNch8REWkYBbqGcJXBuqfgidHYxGfZFjOPSQWL+Sh0Dm/ccRbTT+/h6wpFRI5bmZTB1EUf0//ed5i66GNWJmU06/0uuugicnJyyMnJ4R//+Mfx497BoENLXgZ/GQELI5zH5GW+rqjeZs+ezb333uvrMkRExIsCXX14/+O7eCA8PhLe+yWu3mfwpwH/5sJdlzF2yGm88eOpxEd18XW1IiLHrUzK4L4V35KRU4QFMnKKuG/Ft80a6t59910iIiKqBbqWVF5e7pP71il5Gbx1J+SmAdZ5fOvOJgl15eXlXH/99SQkJDB37lwKCwt5+OGHGT9+PCNGjOCWW27BWgvAE088wbBhw0hISOCqq64C4NixY9x4442MHz+e0aNH8+abb1a7x3/+8x/uuOMOAH7wgx9w5513MmXKFAYMGMBrr712vN3ixYsZP348CQkJPPjgg43+bCIiUjvNoatLxT++ZUXO62OHAMOxMbdwTeplbErP5a4Zg7hrxiD8/DTEUkRa1kNvbWFrZl6t55NScyh1uSsdKypz8cvXknl5fWqN7xnWN4wHLx1e6zUfffRRgoODufPOO7n77rv55ptv+Pjjj/noo4947rnnWLNmDYmJidx777189913jBo1ivPPP5+LL76YgoIC5s6dy+bNmxk7diwvvfRSrcPT4+Pjuf7663nrrbcoKytj+fLlDBkyhCNHjnDjjTeyZ88eOnfuzNNPP01CQgILFy4kMzOTffv2ERUVxQUXXMDKlStxuVxs3ryZn//855SWlvLiiy8SFBTEu+++S7du3erxXT4F790LB76t/Xz61+AqqXysrAjevAM2PF/ze3qPhAvrHua4Y8cO/v3vfzN16lRuvPFG/vGPf3DHHXfwwAMPALBgwQLefvttLr30UhYtWsTevXsJCgoiJycHgEceeYRzzz2XZ599lpycHCZMmMB555130nvu37+fNWvWsH37dmbPns3cuXP54IMP2LVrF+vXr8day+zZs/nss8+YPn16nZ9BREROnXro6vLRwyfC3HGWvI0r2JlVwJPXjuXu809XmBORVqlqmKvreH1Mnz6dzz//HIDExEQKCgooKytjzZo1TJs27Xi7RYsWcdppp7Fp0yYWL14MQFJSEo8//jhbt25lz549fPHFFye9V1RUFBs3buS2227jscceA+DBBx9k9OjRJCcn8/vf/57rrrvuePsNGzbw5ptvsnTpUgA2b97M0qVLWb9+Pb/5zW/o3LkzSUlJTJ48mRdeeKHB34MGqxrm6jp+CmJjY5k6dSoA1157LWvWrOGTTz5h4sSJjBw5ko8//pgtW7YAkJCQwDXXXMNLL71EQIDzt90PPviARYsWMWrUKM4++2yKi4tJTa059FeYM2cOfn5+DBs2jIMHDx6/zgcffMDo0aMZM2YM27dvZ9euXY3+fCIiUjP10NUlN73Gw73I5o3bpzK4d2gLFyQicsLJetIApi76mIycqn+UguiIEF69dXKD7jl27Fg2bNhAfn4+QUFBjBkzhsTERD7//HOeeOIJ/vCHP9T63gkTJhATEwPAqFGj2LdvH2eeeWat7S+//PLj91yxYgUAa9as4fXXXwfg3HPPJTs7m9zcXMCZ4xUSEnL8/eeccw6hoaGEhoYSHh7OpZdeCsDIkSNJTk5u0Oc/qbp60v4ywjPcsorwWLjhnUbdumpPpzGG22+/ncTERGJjY1m4cCHFxcUAvPPOO3z22WesWrWK3/72t2zZsgVrLa+//jqDBw+udJ2KoFaToKCg488rhnNaa7nvvvu49dZbG/V5RESkfhrVQ2eMmWWM2WGM2W2MqTZL2hgz3Riz0RhTboyZ25h7+UphSO8ajxcF91aYE5FW756ZgwmpshdmSKA/98wcXMs76hYYGEh8fDzPPfccU6ZMYdq0aXzyySd89913DB069KTv9Q4A/v7+dc51q2jv3bYiOHirCDNdulSex+x9Pz8/v+Ov/fz8fDPPbsYDEBhS+VhgiHO8kVJTU/nqq68AePnll48H5aioKAoKCo7PcXO73aSlpXHOOefw6KOPkpOTQ0FBATNnzuT//u//jn9/k5KSGlTHzJkzefbZZykoKAAgIyODrKysxn48ERGpRYMDnTHGH/g7cCEwDLjaGDOsSrNU4AfA0obex9ceLfs+hbZTpWOFthOLy7/vo4pEROpvzuho/nD5SKIjQjA4PXN/uHwkc0ZHN+q606dP57HHHmP69OlMmzaNJ598klGjRlXqJQoNDSU/P7+Rn6Dmey9ZsgRwVs6MiooiLCysye/TLBKuhEufcHrkMM7jpU84xxtp6NChPP/88yQkJHDkyBFuu+02br75ZkaOHMmcOXMYP348AC6Xi2uvvZaRI0cyevRo7r77biIiIrj//vspKysjISGBESNGcP/99zeojgsuuID58+czefJkRo4cydy5c5vl50BERByNGXI5Adhtrd0DYIx5BbgM2FrRwFq7z3Ou4ZM1fOz5ggkc8SvllwHL6GuyybTdebT8St4qmcBCXxcnIlIPc0ZHNzrAVTVt2jQeeeQRJk+eTJcuXQgODq40fw6ge/fuTJ06lREjRnDhhRdy8cUXN8m9Fy5cyA033EBCQgKdO3fm+edrWUyktUq4skkCnLf4+Hi2bt1a7fjvfvc7fve731U7vmbNmmrHQkJCeOqpp6odP/vsszn77LMBZ2XLH/zgB4Cz4qW3ih45gLvuuou77rrrFD6BiIg0lKlp6Eq93ugMoZxlrb3J83oBMNFae0cNbf8DvG2tfa3qOa82twC3AMTFxY1NSUlpUF1N7WTzT76491wfVCQiHd22bdvqHNoo0trp51hE5OSMMRustePqateYOXQ1LevYsHQIWGufttaOs9aO69Gj9WzM3RzzT0RERERERJpCY4ZcpgOxXq9jgMzGldP6VAxTWrx6B5k5RfSNCOGemYObfPiSiEhH9b3vfY+9e/dWOvbHP/6RmTNn+qgiERGRtqMxge5rYJAxpj+QAVwFzG+SqlqZ5ph/IiLSGNbaWjfkbmveeOMNX5cgLayh0z1ERKS6Bg+5tNaWA3cAq4FtwDJr7RZjzMPGmNkAxpjxxph0YB7wlDFmS1MULSLSkQUHB5Odna1fiqVNstaSnZ1NcHCwr0sREWkXGrwoSnMaN26cTUxM9HUZIiKtUllZGenp6cc3iRZpa4KDg4mJiSEwMNDXpYiItFr1XRSlMUMuRUTEBwIDA+nfv7+vyxAREZFWoDGrXIqIiIiIiIgPKdCJiIiIiIi0UQp0IiIiIiIibVSrXBTFGHMISPF1HTWIAg77ughpt/TzJc1JP1/SnPTzJc1JP1/S3Frrz1g/a22Puhq1ykDXWhljEuuz0oxIQ+jnS5qTfr6kOennS5qTfr6kubX1nzENuRQREREREWmjFOhERERERETaKAW6U/O0rwuQdk0/X9Kc9PMlzUk/X9Kc9PMlza1N/4xpDp2IiIiIiEgbpR46ERERERGRNkqBTkREREREpI1SoKsHY8wsY8wOY8xuY8y9vq5H2g9jTKwx5hNjzDZjzBZjzF2+rknaH2OMvzEmyRjztq9rkfbHGBNhjHnNGLPd8/9lk31dk7Qfxpi7Pf8+bjbGvGyMCfZ1TdJ2GWOeNcZkGWM2ex3rZoz5rzFml+cx0pc1NoQCXR2MMf7A34ELgWHA1caYYb6tStqRcuDn1tqhwCTgx/r5kmZwF7DN10VIu/VX4H1r7RDgDPSzJk3EGBMN3AmMs9aOAPyBq3xblbRx/wFmVTl2L/CRtXYQ8JHndZuiQFe3CcBua+0ea20p8ApwmY9rknbCWrvfWrvR8zwf5xehaN9WJe2JMSYGuBh4xte1SPtjjAkDpgP/BrDWllprc3xblbQzAUCIMSYA6Axk+rgeacOstZ8BR6ocvgx43vP8eWBOixbVBBTo6hYNpHm9Tke/cEszMMbEA6OBdb6tRNqZx4FfAm5fFyLt0gDgEPCcZ1jvM8aYLr4uStoHa20G8BiQCuwHcq21H/i2KmmHellr94Pzh3agp4/rOWUKdHUzNRzTXg/SpIwxXYHXgZ9aa/N8XY+0D8aYS4Asa+0GX9ci7VYAMAb4p7V2NHCMNjhcSVonz1ymy4D+QF+gizHmWt9WJdL6KNDVLR2I9Xodg7r7pQkZYwJxwtwSa+0KX9cj7cpUYLYxZh/OcPFzjTEv+bYkaWfSgXRrbcXIgtdwAp5IUzgP2GutPWStLQNWAFN8XJO0PweNMX0API9ZPq7nlCnQ1e1rYJAxpr8xphPOZNxVPq5J2gljjMGZe7LNWvtnX9cj7Yu19j5rbYy1Nh7n/7s+ttbqr9vSZKy1B4A0Y8xgz6EZwFYfliTtSyowyRjT2fPv5Qy06I40vVXA9Z7n1wNv+rCWBgnwdQGtnbW23BhzB7AaZ3WlZ621W3xclrQfU4EFwLfGmE2eY7+21r7rw5pERE7FT4Alnj967gFu8HE90k5Ya9cZY14DNuKsCp0EPO3bqqQtM8a8DJwNRBlj0oEHgUXAMmPMD3H+iDDPdxU2jLFW08FERERERETaIg25FBERERERaaMU6ERERERERNooBToREREREZE2SoFORERERESkjVKgExERERERaaMU6EREpN0yxriMMZu8vu5twmvHG2M2N9X1REREGkL70ImISHtWZK0d5esiREREmot66EREpMMxxuwzxvzRGLPe8zXQc7yfMeYjY0yy5zHOc7yXMeYNY8w3nq8pnkv5G2P+ZYzZYoz5wBgT4rMPJSIiHZICnYiItGchVYZcft/rXJ61dgLwN+Bxz7G/AS9YaxOAJcATnuNPAP+z1p4BjAG2eI4PAv5urR0O5ABXNPPnERERqcRYa31dg4iISLMwxhRYa7vWcHwfcK61do8xJhA4YK3tbow5DPSx1pZ5ju+31kYZYw4BMdbaEq9rxAP/tdYO8rz+FRBorf1d838yERERh3roRESko7K1PK+tTU1KvJ670Nx0ERFpYQp0IiLSUX3f6/Erz/Mvgas8z68B1niefwTcBmCM8TfGhLVUkSIiIiejvySKiEh7FmKM2eT1+n1rbcXWBUHGmHU4f9y82nPsTuBZY8w9wCHgBs/xu4CnjTE/xOmJuw3Y3+zVi4iI1EFz6EREpMPxzKEbZ6097OtaREREGkNDLkVERERERNoo9dCJiIiIiIi0UeqhExGRFmGMiTfGWGNMgOf1e8aY6+vTtgH3+rUx5pnG1CsiItIWKNCJiEi9GGNWG2MeruH4ZcaYA6cavqy1F1prn2+Cus42xqRXufbvrbU3NfbaIiIirZ0CnYiI1Nd/gAXGGFPl+AJgibW2vOVL6lga2mMpIiLtlwKdiIjU10qgGzCt4oAxJhK4BHjB8/piY0ySMSbPGJNmjFlY28WMMZ8aY27yPPc3xjxmjDlsjNkDXFyl7Q3GmG3GmHxjzB5jzK2e412A94C+xpgCz1dfY8xCY8xLXu+fbYzZYozJ8dx3qNe5fcaYXxhjko0xucaYV40xwbXUfJox5mNjTLan1iXGmAiv87HGmBXGmEOeNn/zOnez12fYaowZ4zlujTEDvdr9xxjzO8/zs40x6caYXxljDgDPGWMijTFve+5x1PM8xuv93YwxzxljMj3nV3qObzbGXOrVLtDzGUbV9t9IRERaPwU6ERGpF2ttEbAMuM7r8JXAdmvtN57XxzznI3BC2W3GmDn1uPzNOMFwNDAOmFvlfJbnfBjO3nB/McaMsdYeAy4EMq21XT1fmd5vNMacDrwM/BToAbwLvGWM6VTlc8wC+gMJwA9qqdMAfwD6AkOBWGCh5z7+wNtAChAPRAOveM7N87S7zvMZZgPZ9fi+APTGCdL9gFtw/u1+zvM6DigC/ubV/kWgMzAc6An8xXP8BeBar3YXAfuttd779ImISBujQCciIqfieWCeMSbE8/o6zzEArLWfWmu/tda6rbXJOEHqrHpc90rgcWttmrX2CE5oOs5a+4619jvr+B/wAV49hXX4PvCOtfa/1toy4DEgBJji1eYJa22m595vATX2Wllrd3uuU2KtPQT82evzTcAJevdYa49Za4uttWs8524CHrXWfu35DLuttSn1rN8NPOi5Z5G1Ntta+7q1ttBamw88UlGDMaYPTsD9kbX2qLW2zPP9AngJuMgYE+Z5vQAn/ImISBumQCciIvXmCSiHgMuMMQOA8cDSivPGmInGmE88wwFzgR8BUfW4dF8gzet1pbBjjLnQGLPWGHPEGJOD07tUn+tWXPv49ay1bs+9or3aHPB6Xgh0relCxpiexphXjDEZxpg8nJBUUUcskFLLXMJY4Lt61lvVIWttsVcNnY0xTxljUjw1fAZEeHoIY4Ej1tqjVS/i6bn8ArjCM0z0QmBJA2sSEZFWQoFORERO1Qs4PXMLgA+stQe9zi0FVgGx1tpw4EmcYYp12Y8TRirEVTwxxgQBr+P0rPWy1kbgDJusuG5dG6pm4gxPrLie8dwrox51VfUHz/0SrLVhOEMYK+pIA+JqWbgkDTitlmsW4gyRrNC7yvmqn+/nwGBgoqeG6Z7jxnOfbt7z+qp43lPzPOAra21DvgciItKKKNCJiMipegE4D2feW9VtB0JxeoiKjTETgPn1vOYy4E5jTIxnoZV7vc51AoJwegbLjTEXAhd4nT8IdDfGhJ/k2hcbY2YYYwJxAlEJ8GU9a/MWChQAOcaYaOAer3PrcYLpImNMF2NMsDFmqufcM8AvjDFjjWOgMaYiZG4C5nsWhplF3UNUQ3HmzeUYY7oBD1acsNbux1kk5h+exVMCjTHTvd67EhgD3IVnIRsREWnbFOhEROSUWGv34YShLji9cd5uBx42xuQDD+CEqfr4F7Aa+AbYCKzwul8+cKfnWkdxQuIqr/Pbcebq7fGsYtm3Sr07cHql/g84DFwKXGqtLa1nbd4ewglEucA7Vep0ea49EEgF0nHm72GtXY4z120pkM+JFUPBCVeXAjnANZ5zJ/M4zhzAw8Ba4P0q5xcAZcB2nMVkfupVYxFOb2d/79pFRKTtMtbWNVJFRERE2gtjzAPA6dbaa+tsLCIirZ42KBUREekgPEM0f4jTiyciIu2AhlyKiIh0AMaYm3EWTXnPWvuZr+sREZGmoSGXIiIiIiIibZR66ERERERERNqoVjmHLioqysbHx/u6DBEREREREZ/YsGHDYWttj7ratcpAFx8fT2Jioq/LEBERERER8QljTEp92mnIpYiIiIiISBulQCciIiIiItJGKdCJiIiIiIi0UQp0IiIiIiIibZQCnYiIiIiISBulQCciIiIiItJGtcptC0REREREpP5WJmWwePUOMnOK6BsRwj0zBzNndLSvy5IWoEAnIiIiItKGrUzK4L4V31JU5gIgI6eI+1Z8C6BQ1wFoyKWIiIiISBu2ePWO42GuQlGZi8Wrd/ioImlJCnQiIiIiIm1YRk5RrceXfZ1GQUl5C1ckLUmBTkRERESkDcrMKeLHSzfWej7Az/DL15OZ8MiH/GL5N6zbk421tgUrlJagOXQiIiIiIm1ISbmLZz7fy98+3o3bWmYN782nO7MoLnMfbxMS6M/vvzeCuO6dWZ6YztvJ+3ltQzrx3Tszd2wMV4yNoU94iA8/hTQV0xpT+rhx42xiYqKvyxARERERaVU+2Z7FQ29tYV92IbOG9+Y3Fw8ltlvnOle5LCwt571vD7B8Qxpr9xzBGJg2qAfzxsZw/rBeBAf6+/BTSU2MMRustePqbKdAJyIiIiLSuqVkH+O3b2/lw21ZDOjRhYWXDmf66T0adK3U7EJe25DGaxvSycwtJjwkkMtG9eXKcbEM7xuGMaaJq5eGUKATEREREWnjikpd/PPT3Tz52R4C/Qx3zhjEDVP70ymg8UthuNyWL787zPLEdN7fcoDScjdDeocyb1wsc0b1pXvXoCb4BNJQCnQiIiIiIm2UtZbVWw7w27e3kZFTxGWj+nLfhUPpHR7cLPfLLSxjVXImyxPTSE7PJdDfMGNIL64cH8P0QT0I8Ndaii1NgU5EREREpA3anVXAQ29t4fNdhxnSO5SHZg9n4oDuLXb/HQfyWZ6YxhtJGWQfK6VnaBDfGxPNvLGxDOzZtcXq6OgU6ERERERE2pCCknKe+GgXz67ZS0gnf35xwWCumRjns96x0nI3n+zIYnliGp/sOITLbRkTF8GV42K5OKEPocGBPqmro2jSQGeMmQX8FfAHnrHWLqpy/kfAjwEXUADcYq3daoyJB7YBFdvUr7XW/qiu+ynQiYiIiEhHYa3lzU2Z/P7dbWTll/D9cbHcM2swUa1oDltWfjFvbMxg+YZ0dmcVEBLoz4UjezNvbCwT+3fDz08LqTS1Jgt0xhh/YCdwPpAOfA1cba3d6tUmzFqb53k+G7jdWjvLE+jettaOOJXiFehEREREpCPYmpnHwlVbWL/vCGfEhPPQZSMYFRvh67JqZa1lU1oOyxLTefubTPJLyontFsK8sbFcMTaG6AjtbddU6hvo6rOx+ARgt7V2j+fCrwCXAccDXUWY8+gCtL5xnCIiItKk6tr3SirT90u85RaW8ef/7uDFtSlEdO7EH68YybyxsQ3v6UpeBh89DLnpEB4DMx6AhCubtmjAGMPouEhGx0XywCXDWL3lAMsS0/jzf3fylw93cubAKOaOjWHm8N7a266F1CfQRQNpXq/TgYlVGxljfgz8DOgEnOt1qr8xJgnIA/6ftfbzhpcrIiIircHKpAzuW/EtRWUubvLtbwAAIABJREFUADJyivjl68nszsrnzEEN2xurPVuz6xBPf76X0nI34Hy/7lvxLYBCXQfjdluWJabx6Ood5BSWsmBSP352/mDCOzdiPlryMnjrTigrcl7npjmvoVlCXYWQTv7MGR3NnNHRpB0p5LUN6by2IZ27XtlEWHAAs0f1Zd7YWBJiwrW3XTOqz5DLecBMa+1NntcLgAnW2p/U0n6+p/31xpggoKu1NtsYMxZYCQyv0qNX8b5bgFsA4uLixqakpDTmc4mIiEgzsdYy/pEPOVxQ6utS2ry+4cF8ed8MX5chLWRTWg4PvrmZb9JzGR8fyUOzRzCsb9ipXcRaKMiCQ9sga7vzuGkpuGr436Pxh9iJEBEHEbGexzgIj3V68QKafo6e2235ak82yxPTeG/zAUrK3QzuFcq8cTHMGR3dquYFtnZNOYduMrDQWjvT8/o+AGvtH2pp7wcctdaG13DuU+AX1tqTTpDTHDoREZHWJ7ewjBVJ6Sxdl8qurIIa2xhgyc3VBvJ0ePP/ta7Wczed2Z9rJvWjf1SXFqxIWlJ2QQmPvr+DVxPT6BkaxK8vGsplo/rW3WtVcKhycKt4LDp6ok1IZOXXVcVNgZxUyM8E6/Y6YSC094mA5x36wj3PAxs3Hy63qIy3kzNZnpjOprQcAvwM5w7pyZXjYjl7sPa2q0tTBroAnEVRZgAZOIuizLfWbvFqM8hau8vz/FLgQWvtOGNMD+CItdZljBkAfA6MtNYeOdk9FehERERaB2stSWk5LF2XytvJmRSXuTkjJpyU7EJyisqqtY+OCOGLe8+t4Uod29RFH5ORU1TteEigH2UuS7nbMm1QFAsm9ePcIT31i247Ue5ys2RdKn/6YAeFpS5uPLM/d84YRNegKrOejh2GrG1waPuJx0PboTD7RJvgcOgxFHoOqfzYtSc8PtIZZllVeCzcvdl57iqDvAzISXMCXq7nseIrLwPc5ZXf36VHlcDn1cMXEQtBofX+Xuw6mM/yDems2JjO4YJSoroGcfmYaOaNjWFQr/pfpyNp6m0LLgIex9m24Flr7SPGmIeBRGvtKmPMX4HzgDLgKHCHtXaLMeYK4GGgHGdLgwettW/VdT8FOhEREd/KKy7jzaQMlqxLZfuBfLp08uey0dHMnxDHiOjwanPoAEIC/fnD5SM1J6wGJ/t+TRnYnVfXp7F0fSr7c4vpGx7M/IlxfH98HD1CNTytrVq3J5sHV21h+4F8zhwYxcLZwxjYtcwT2Cp62zwBrvDwiTcGhUGPIdWDW2hvqK1Hr+ocOnB61y59ov5z6NwuyN/vFfgqwl7F63RwlVR+T0ikV8iLq9LLFwsh1VfrLHO5+XTHIZYlpvHJ9izK3ZZRsc7edpec0Ycw7W13nDYWFxERkVOWnJ7DkrWprPomk6IyF8P7hjF/YhyXjYqu1qugVRtPTV3fr3KXm4+2Z/HS2hQ+33WYQH/DrBF9WDCpH+PjI7WoRBtxMK+Yv7y1nt2bv2ZClyy+H19AnCsVk7UdjmWdaNgpFHoMrh7cwvrWHtxOprlXuXS7nfq9e/WO9/J5Hsur9EIHhVcOeFWGdR52d2HlpkyWJaax82ABwYF+XDiiD/PGxjBpQPcOv7edAp2IiIjUS0FJOas2ZbJ0fQqbM/IICfRn9hl9mT8xTqvT+cieQwUsWZfK8sQ08orLGdwrlGsn9+N7o6sHa/Gh4tzj89pcB7exf1cSnY7spKfxmtPWqasT3I6HNs9XeEzDgltrZa0zRDQnpYZhnZ7H0vzK7wnsAhGx2Ig4sv17kpgbyof7g9hd0g13eAznjh3BFWNjie3W2TefyccU6EREROSkNmfksnR9Km8mZXCs1MWQ3qFcMzGOy0ZHa9hTK1FU6uKtbzJ5Ye0+Nmfk0aWTP5ePieHaSf0Y3FvzjlpMcR4c2lF9gZL8zONNighipzuagrCBDDtjIpH9EpwAFxYDfpoTibVQnFNlGGeVeXzFOZXeUmwDybBRHAvpS2jvAcT0H0xgt/gTvXxde7fr760CnYiIiFRTWFrO29/sZ8m6FL5JzyUowI9LEpzeuDFxEeqNa6WstXyTnsuLX6XwVnImpeVuJvTvxoJJ/Zg5vDedAtrvL7UtqiTfCW7eC5NkbYe89BNtAkKgx+nQYyi5oafx0nedeTmlC4GRcTwweyTnDOnpu/rbuuI8T8hzgl7+wT0cSNlJ2dEUerqyiDJVdj7zC3R6Oiutzuk1rDO0L/ifpEe7hTZjbygFOhERETlu+4E8lq5L5Y2NGeSXlDOoZ1fmT4zj8tExjdvQWFrc0WOlLN+QxktrU0k9UkhU1yCunhDL1RPi6BvRuGXmO4zSYyfCmvcCJd4rRfoHHQ9ulea5RfSj2AVP/W8P//h0N37GcMe5A7lpWn+CAvx995naMbfbsm7vEd5cv5PkrVvo4cpiTFgeZ/UsYkhILsHH0p0QWHCg8huNP4RF1zyP7+Bm+Ph3jVtIppkp0ImIiHRwxWUu3k7ez9J1KWxMzaFTgB8XjejNNZP6Ma6fFtlo69xuy2e7DvHS2hQ+2p6FAc4b2ovrJscz5TQtKAFAaSEc3lF9H7ec1BNt/DtB1OlVVpYcCpHx4Fc5oFlr+XBbFg+/vYW0I0VcnNCH31w0VEG6BeUXl/F28n6WJ6axMTUHfz/DOYN7Mm9cDOcODCOwYH/t8/iq7cVXA++tHnxMgU5ERKSD2p2Vz5J1qby+IZ284nIGRHVh/sQ4rhgTQ2SXTr4uT5pB2pFClq5P5dWv0zhyrJT+UV24ZmIc88bGtt0e2FMZDldWBId3Vg9uR1MAz++6foEQNcgT3IaeeIzsf/JheR57Dx/jobe28OmOQwzq2ZWHZg9nysCopvu8csp2Z1XsbZfBofwSorp2Ys6oaOaNi615junxvfhS4flLa7mqgYU5tZxrWQp0IiIiHUhxmYv3Nx9g6bpU1u87cnzJ+/kT4pg0oJt64zqIknIX7317gJfWppCYcpTgQD9mn9GXBZPiGRkT7uvy6q+2fdUu+hP0SaghuO070fPiFwDdB1YPbt0GgP+ph9tjJeX87ZPd/PvzvQQF+PHT80/nusn9CNTm761GucvN/3YeYnliOh9uO0i523JGTDjzxsVy6Rl9CQ+p4b/7X0bUvRm7jynQiYiIdADfHSrg5XWpvL4xnaOFZfTr3pmrJ8Qxd2wMUV21KXVHtjUzj5fWpbAyKYPCUhdnxEawYFI/LknoQ3BgK57rZS38eVilFSRrZPyd4FZ1H7fupzUouFUvw/J28n4eeWcbB/KKuWJMDL+6cDA9Q4MbfW1pPtkFJazclMnyxDS2H8gnKMCPmcN7c+W42MpDkZtiM/ZmpkAnIiLSTpWUu1i95SBL16Wwds8RAvwMFwzvxfwJ/TR3SqrJKy7jjY0ZvLg2hd1ZBUR0DuT742KZPzGOft27+KaosiJn2NvRfXBkr/N4tOIxpfoG1d7mPusJbgMhoHmGEO84kM+Dqzazds8RhvcN4+HLhjO2X7dmuZc0D2stmzPyWL4hjZVJGeQVlxMdEcIVY2OYNzaG2G6d+XrVU8RuXExPe5gsE0XamHsYP/tWX5d+nAKdiIhIO5OSfYyl61N5LTGd7GOlxESGcPWEOOaNi1GvgdTJWstXe7J5aW0Kq7ccxG0tZ53egwWT+nH24J74N+UfAqyFY4erBLV9J8Jb1d63wC7OIiTd+juPSS9V25MMaPbhcHnFZTz+3108/9U+QoMDuGfmYK4aH9e03xtpccVlLv679SDLEtNYs/sw1sLAHl1IOVJImetEFgoJ9OcPl49kzuhoH1Z7ggKdiIhIO1DmcvPfrQdZui6VNbsP4+9nmDGkJ/MnxjF9UA/f9sa18j2cpHYH84p5eX0qL69P5WBeCdERIVwzKY4rx8XWf6hueakzB+noXq9eNq+v0oLK7UP7OAuQRMZXDm+R/aFLFHjP82zh4XBut2VFUgaL3ttG9rFS5k+I4xcXDNYiQu1QZk4RKzam85cPd+FyV89B0REhfHHvuT6orDoFOhERkTYs7Ughr3ydyrLEdA7ll9A3PJirJji/cPcObwW9ccnLYNWdlYfGBYTA7NYz/0TqVuZy8+HWg7y4NoUvv8sm0N9w0cg+LJjUj7H9IjFFR2vpZUtxNtv2XgI+IPhEWKsIahXBLSLOCWSnooX+YLA5I5cH3tzMxtQcRsdF8NvLRjAiug0tICMN0v/ed6gpBRlg76KLW7qcGtU30NW9RquIiIi0iHKXm4+2Z7F0XSqf7TqEAc719MaddXoTD4mrS1mxMywuN8NZ5jsvo/Lzg1uq7+dUXgQrbnF+Ce/cDTp393xFeR69j3U/cawJFrCQhgnEzYUxJVw4o5isgWns3P4thdt2E7T1AAX+hwi1xyq/oUtPJ6TFTfLqYYt3wlvXXuDXhCs/JlzZrH8cOHqslMUf7ODl9al079KJx+adweWjozUHtYPoGxFCRk71uZptcU9BBToREREfy8gp4tX1qbyamMbBvBJ6hQXxk3MHcdX42Ob55aK8BPIyPV81hLXcDCg8XP19Id0gLBrCo+HAt7Vc3EL8NCjMdr6O7IXCI1CSW3s9QeE1hD3P6y5R1UNgcETTBof2rjivci+b9/DI3DRwlwPQE+jpF4i7exwH/KP5NG84mwoiOBzQh8HDzmDmmRM4Lbq37z5HE3G5LS+vT+WxD3aQX1zODVP689PzBxEWrD8sdCT3zBzMfSu+pajMdfxYSKA/98wc7MOqGkZDLkVERHzA5bZ8usPpjftkRxYWOOv0HsyfEMe5Q3oS0NA9rlxllcNabnr158eyqr8vOOJEWAuLrv48rC906nyi/anu4VReCkVHTgS9418nOVZWWPNnNH4QEllzAKztKyi08hyt9sTtdnpTK81j8wpvRUcqtw/pVvM8tsh457+zn7OlgbWWjak5vLQ2hXeS91PqcjN5QHcWTO7H+cN6tcl92DakHOGBN7ewJTOPyQO6s3D28Jo3oJYOYWVSBotX7yAzp4i+ESHcM3Nwq1kQBTSHTkREpFU6kFvMq1+n8erXqWTmFtMjNIgrx8Vw1fg4Yrt1PvmbXeWQv98T0NI9vWpVnhcchKozQ4LCnV/UTxbWgrqe2gdpiUUrSgtPhMBjh2sJf1VCobus5mv5BdYj/FU53qmO/x6nqjFzwkqPOfPWauply0kBV+mJtsYfImIrBzXv8BZ86vPDsgtKWJaYzpJ1KaQfLaJnaBBXT4jj6glxrWNOZx2y8ov543s7eH1jOr3Dgvl/lwzl4pF9MO015Eu7oEAnIiLSSrjcls92HWLpulQ+3p6Fy22ZNiiK+RPiOK+ip8PtgvwDtQ+BzMtwwlrVeWudQj3hrK8nqMVUfx7UTD0QrW2VS2uhJP8kPX81hcIjVAvAFQJCPEM+a5n7V/VYSLfa90WrKwBb6/z3ra2XrWqvalBYlR62+BPhLTwW/JtnVo3Lbfnfzixe/CqFT3cews8YLhjWiwWT+jH5tO6tLiCVudw8/+U+/vrhLorLXdw8bQA/PmcgXYI060haPwU6ERERH8vKL2Z5YjqvrttLcc5BhnbOZc5phrN6l9Kt/FDlsJZ/AKyr8gUCu3iFtZianzegt0W8uF1QnFtLz18NwfBYdh3zAcNqWBCmGyS96NynqoBgJ4gd3VdlM23jhGTvVSO9h0eGRPp8CGlqdiFL1qew7Os0jhaWMaBHFxZM6sflY2IID/H9fLQvdx/mwVVb2JVVwNmDe/DAJcMY0OMUe6JFfEiBTkRE5FQ1pMfJ7YZjh44Pe3TnZpCZupsDad9BXia9TTa9zVECqBLWAkJqGAJZNaxF+PyXdqmBq+zkc/9qnA94rPbrDbmkei9bRCwE1HM/OB8rLnPx7rf7eXFtCkmpOfz/9u48PMrybvv498pkX9gSEiAJhCUsYYeIIIKKCiiiuFStorg81cdqXdrSotZqrba4PLbFperbulWr4lKKW0EBq7iSEGRfQ4CELRASCNlnrvePGSBAIJOQ5M4k5+c4cszMfV/3zG9whDlzbREhLqYM7cLUkd3o36Xpf+GwvbCURz9ew0fLd5DcIYIHL+rPuf3im13voUhtFOhERETq4kRD4s5/FJKG1TxfbX8u7N9x3LytMhvCbhOLJyaR2C4pxMSnHD93rRn0sEgTeqq/9/NyrBMtIhOgVuYV8fq3W5izLI+ySg/DurbjulHduGBAZ8JDXI362uVVbv725WaeWbgRj7Xcfk4vbhnbo9FfV6SxKNCJiIicjLXenpNDwx7n3AZlhbVf5wo93JNm23Qhz9OBr/LDWLg9mFx3LJ2SezJ55AAmDmz8L7ASQJpiEZlmpKi0kvcyc3n92y1k7zlIh6hQrkxP5trT/Vj8px4Wrd3N7z5YRc7eEib278T9k/o1yuuINCUFOhERab2shdJ91eao+Zbrr77QyP7tUFXm3/Nd/c8jwyGj4igo8X5ZffP7rWTvOUib8GAuH57ENSO6kpqgJdDlBJrbIjJNwFrL15v28o9vtvDpml14rOWcPvFcN7IbY3t3xHWKm3hv2XuQ33+4ms/W7KZHxygemtyfsb07NlD1Is5SoBMRkZbJWm9P2uGAllvz/aMWmACCgiGm84nnq7011buX17F8Q+KstSzJ2ccb323hkxU7qXB7GN6tPdeM6MqkQeqNE6nNjqJS3vx+G29+v5X8A+Ukd4jg2tO7cWV6Mh2iTrA66AmUVrj56+cbef6LbEKCDHeem8qNo7sTGhx4e+OJnIgCnYiIBKayohPPVzt0/9gFJkzQMWGthr3WouMPb5hco+Wzqfr3zwh2H+m1q3KFU3HBn3mrbCT//H4rG3cXExMWzKXDErnm9K707dSmkf4QRFquSreH+at28Y9vc/g2u4DQ4CAuGtiZqaO6MTS53UkXL7HWMm/VTn7/4RryCkuZMqQL917Yj4Q2zX8vPJG6UqATEZHmp/xAzfurHb6/HSoOHH2NCYLoTtU2xk46/n50winvuzUnK4/F/3qOu3mLLmYv220sT7qv4gPPaNwWBie349oRXblocGciQ7WHlUhD2LDrAK9/u4X3luZRXF5F/y5tuG5kNy4e0oX5q3bxxLx1bC8spUu7CK4f1Y3FG/fw5YY99O0Uw+8u7s/pPWKdfgsijUaBTkREmlbFwZMPgdy/vYb9u4w3jJ0srMV0Alf99rSy1lLh9lBS7qak0k1JeRUlFW4OVlRRWuHmYIWb0ooqDpa7+fNn69lfVnXcc0SGunjnf0c5svy6SGtxsLyKOcvy+Mc3W1i78wBhwYYqj3cj8+rCgw33XpjGtad3Jdil4ZXSsvkb6PQrRhERqV1FSbVl+k8wd62mTZOj4r2hLLYndB/rC2tJvmGQXbzDJIND8XgsZVVuDpa7fUHLG7xK8qsoydt7+FhphbdNSWWVN6RVuCk51Pbwre++L8Qd+4Wwrkor3ApzIo0sKiyYa0/vxjUjupK5ZR/X/f173B73ce3aRYUx7YyUpi9QpBlToBMRacGWzH2B5KVPEG/z2W06sm3YdE67+NajG1WW+sJa3vGrQB4Ka6X7jntud0QsFVGdKYvoTEnyUA6EJVAUEs++4I7sdcWxhw4cqHId7gUr2eOmZPuh4FVJSUU2JRXrD4ewuggNDiIy1EVUaDARoS6iQl1EhLpIaBNOZKjL9xPsbRMWTESIi6gwFxGhwYfbRvnOH7o/adaXbC86ftXLLu0i6lSbiNSfMYb0lA6UVdb8d8KuGv4fFWntFOhERFqoJXNfYEDmb4gwFWCgE/nEZt7LxvWziYiMJrx0J5FlO4moPH7vtf1Bbdhj4thlYtlhTyfXdGBbVTu2uTuw3cayy7anvCwUjs95PsVA8XHh6tD92OgwX7DyBqzIUBeRYcE1tj3qfpiLyBBXowy1+tXEvtz7/gpKq32RjAhxMX1CnwZ/LRE5uS7tIsgrLK3xuIgcTYFORKSF2Z23ma1L55OW+VtvmKsmxLjpfiCT9fuTWWtj2WmHs5NYCoLj2Bccz4HQBA6GdcQVFlUtTHlvO4S5SAoJ9vV0HekdO6o37FCPWZiL8GAXQae4x1RTmjI0EeCoRRimT+hz+LiINJ3pE/roFywiflKgExEJcDu2rCM36zNszmK6FC0lye4kHjjRzDEDtPn593QKcREZ5iLUFXTSZcJbkylDExXgRJoB/YJFxH8KdCIiAcR6PGzPWUfeD59icr4iqSiTzuTTGSgiiuzIweQmXkNs/3HEzLmBTuw57jl2mzgSNWxJRJo5/YJFxD8KdCIizZj1eMjdtIIdPywgaOtXJO/PIpG9JAL7aMPmqMFsSbqRjgPOpXvaaQx1Hdk4e8nWX9H20Bw6n1Ibyrbh0+nkwHsRERGRhqdAJyLSjFiPh63rsti5YgHB276m24EskikkGdhDO7ZEDyEn+QziB55Ltz5DGVYtwB3rtItvZQn4Vrncw24Tx7bhNaxyKSIiIgHLr43FjTETgb8ALuBv1tqZx5z/X+B2wI13abNbrLWrfefuBW72nbvTWjuvttfTxuIi0lp43G5y1mSwe8UCQnO/IeXgMjqwH4DddGBrzDDcXc+g06Bz6Zo6CBOkjXRFRERagwbbWNwY4wKeBc4HcoElxpi5hwKbzz+ttc/72l8MPAVMNMakAVcD/YEuwGfGmN7W2rptOCQi0kK4q6rYvOpb9qxcSFjeN3QvWU4PiukB7KAjm9qOYmO30XQZfB6J3fsRrwAnIiIiJ+HPkMsRwEZrbTaAMeYt4BLgcKCz1u6v1j6KI4urXQK8Za0tBzYbYzb6nu+bBqhdRKTZq6qsYNOKb9i3agER27+le+kKelFCLyDPJLC+3VhMymgSh5xPl5Q+dHa6YBEREQko/gS6RGBbtce5wOnHNjLG3A78HAgFxlW79ttjrq1xuSJjzC3ALQBdu3b1oywRkeansqKcTT98yb7Vi4jc8R09S1fSx3g3x91murC2w7kEpZxJ8rDzSUzqWfNfiCIiIiJ+8ifQ1bQ50XET76y1zwLPGmOuAX4DTPP3Wt/1LwIvgncOnR91iYg4rryshE3LvqBozSKid35Hz7LV9DXlAOQEJbMqbgLBPcbQbej5JHfpRrLD9YqIiEjL4k+gy4WjvoMkAdtP0v4t4K/1vFZEpFkrKylmY9YiDqz9L212fUfP8jWkmUoAsoNSWBE/mZCeY+g27HxS4hNJcbZcERERaeH8CXRLgFRjTHcgD+8iJ9dUb2CMSbXWbvA9nAQcuj8X+Kcx5im8i6KkAt83ROEiIk2hpLiITUsXUrzuv7Tb/T09K9YxwFThtobNwT1YlnA5ob3G0GPY+fSIS6CH0wWLiIhIq1JroLPWVhlj7gDm4d224CVr7SpjzMNAhrV2LnCHMeY8oBLYh3e4Jb52s/EuoFIF3K4VLkWkOTtQVED20gWUrP+CDvnf06NyAwONmyobRHZILzI7X0VE6li6Dz+fXu1i6eV0wSIiItKq+bUPXVPTPnQi0lSK9u1hc+ZnlG78L3F7ltCjciMuY6mwLrJDe7Ov4wgie59Fz2HjiG7T3ulyRUREpJVosH3oRERaksI9O9m89DPKN35B3N4MelRlM8RYKmwwG0P78X3yTUT3OYuew86hb1Qbp8sVEREROSkFOhFp0fbuymXL0k+p3PQl8QWZdPfkMBQosyFsCkvju8Sf0KbP2fQcehZpkdFOlysiIiJSJwp0IhJQlsx9geSlTxBv89ltOrJt2HROu/jWw+f3bN9CTtZ83NmL6bQvk26ebcQCJTaMTeH9+abzBbTrdw49Bo+hf3ikc29EREREpAEo0IlIwFgy9wUGZP6GCFMBBjqRT7vM+/km52tctorOhUtJttuJA4ptBJsiBrC9yxTa9zuHnoPPZGBomNNvQURERKRBKdCJiKOstZRXeSiv9FBe5aas0kNZlZvy6reVbsqq3Axb+rg3zFUTbioZVTCH/USRHTGI3MSriE0bR4+BIxkcEurQuxIRERFpGgp0Ig6bk5XHE/PWsb2wlC7tIpg+oQ9ThiY2eR3WWircHsqrvAGqesA60W1ZpftIe9/tofPltbSrfuuPFLODRaF7wBx/zmMh6oGtDAnWX2kiIiLSuujbj4iD5mTlce/7Kyit9G7PmFdYyr3vr8Dt8TBhQGdfQKohBDVEyDq2F6zKzansYhLiMoQHuwgLcREWHER4SBBhwS7CQ4IID3EREx5M+OFzrsP3w446duSasGAXEUFVdNqxgIQNbxKz/WtOVN5u05FOCnMiIiLSCukbkEgTK69yk51/kPW7DvDAnJWHw9whpZVufvHOcn7xzvJ6Pb8ryBDuC0iHglJotcDULjL0uLAVdkz7sBqOhfvC11HXBrsI8z12BdXQdVZfBZth6auQ9ToczIe2XWHcb/ghH/osP3rYZakNZdvw6XRquFcXERERCRgKdCKNpKLKQ/aeYtbvKmbDrgOs33WADbuKydl7EI8fPWH3Xdj3uLAVFuI6HKIO31YPW8FBBLuCGv/NNQZ3Jaz7BDJfhk0LwQRB7wsg/UboOQ6CXAwBloS08a1yuYfdJo5tw49e5VJERESkNTH2VMZYNZL09HSbkZHhdBkifql0e9i8x9vjVj285ewtwe1LbkEGUmKjSE2IpndCDKkJMfROiOaml5ewvajsuOdMbBfBVzPGNfVbcUbhVsj09cYV74Q2iTBsGgy7Dtp0cbo6EREREUcYYzKttem1tVMPnYifKt0etuw9yPpdxYd729bvOsDmPQep8gU3Y6Bbh0hSE2KYOKCTN7zFx9CjYxThIa7jnvNXE/seNYcOICLExfQJfZrsfTnCXQUb5nt74zZ86j2WOh7S/wy9zgeX/moSERER8Ye+NYkco8rtYUtBia+n7Uh4y95TTKX7SHBLbh9J74RozktLoHdCNKnxMfSKj64xuJ3IodUsm8Mql02iKA+Wvub9ObAdojvB2Okw7Hpol+x0dSJqIwTwAAAgAElEQVQiIiIBR4FOWi23x7K1oMQX2I6Et+z8g1S4jyyln9Q+gt4JMZzdtyO942PoneANbhGh/ge3k5kyNLHlBjgAjxs2LvD2xq3/D1jrnRN34ePQeyK4QpyuUERERCRgKdBJi+fxWLbtK6nW2+YNb5vyi4/aAy2xXQSpCdGM7d2R1Pjow8EtKkz/m9TL/h3eeXFLX4WibRAVD6PvhuHToH2K09WJiIiItAj6piothsdjyd1X6l2cZPcBNu4q9t7uLqas8khw69I2nNSEGEb3iiU1PobUhGhSE2KIVnA7dR4PZC+EjJe9K1ZaN/Q4G8Y/An0uhOBQpysUERERaVH0DVYCjsdjySssZcPuo+e4bdxdfNTiIp3ahJOaEM21p3fzznFLiCE1PpqYcA3xa3DFu4/0xu3LgchYGHU7DL8BYns6XZ2IiIhIi6VAJ82WtZbtRWVHDZPcsOsAG3YXU1JxJLjFx4TROyGGq0ck09u3HUCv+BjaRii4NSqPB3K+8PbGrf0IPJWQMgbGPQD9JkNwmNMVioiIiLR4CnTiOGstO/eXHbWH23pfj1txedXhdnHRYfROiObK9OQj+7nFR9MuUsP4mtTBPbDsDch8BQqyIaI9nH6rtzcuLtXp6kRERERaFQU6aXBzsvJqXIbfWsvuA+XHbcC9YXcxB8qOBLfYqFBSE6K5bFiidwNu3wIl7aMU3BxjLWz5ytsbt2YuuCug6yg4awakXQIh4U5XKCIiItIqGWut0zUcJz093WZkZDhdhtTDnKy84zbKdgUZuraPYO/BCvZXC27tI0O8ge1wb5v3fmy0huo1GyUF8MOb3t64PeshvC0M/rG3Ny6+n9PViYiIiLRYxphMa216be3UQycN6ol5644Kc+Dd7y2vsIwr0pMO97alJsQQFx2KMcahSuWErIVt30HGS7BqDrjLIek0mPJXSJsCoZFOVygiIiIiPgp00qC2F5bWeLzS7eEPlw5s4mqkTkoLYfnb3mGV+WsgNAaGXQfDb4ROA5yuTkRERERqoEAnDSouJoz8A+XHHe/SLsKBaqRW1kJuBmS+DCvfh6pS6DIMLn4aBlwOoVFOVygiIiIiJ6FAJw3GWktMWPBxgS4ixMX0CX0cqkpqVLYfVsz29sbtWgmh0TD4Km9vXJchTlcnIiIiIn5SoJMGM3/1LrL3HOSKYYl8k11w3CqX0gzkLfX2xq14DyoPQqdBcNGfYOCPICzG6epEREREpI4U6KRBVFR5mPnJWnp2jGLm5YMIdgU5XZIcUl4MK97xBrkdP0BIJAy4DIbfBInDQAvTiIiIiAQsBTppEG98t4XNew7y92npCnPNxY7l3hC3/B2oOADx/eHCJ2HQld7tB0REREQk4CnQySkrKqnkLws2cEbPWMb1jXe6nNat4qB3cZPMlyEvE4LDof9lkH6jd+sB9caJiIiItCgKdHLKnv18I0Wlldw/qZ/2lXPKrtXeEPfD21BeBHF9YOJMGHw1RLR3ujoRERERaSQKdHJKthWU8MpXOVw+LIn+XTSMr0lVlno3/s582bsRuCsM0i7x9sZ1HaXeOBEREZFWQIFOTsnM/6zFFWT45XhtS9Bk8td5txv44U0oK4TYXjD+URhyDUR2cLo6EREREWlCCnRSb5lb9vHR8h3ceW4qndqGO11Oy1ZVDqvnenvjtnwFQSHQb7K3Ny5ljHrjRERERFopBTqpF2stj3y0mo4xYdw6tofT5bRcezZ6Q9yyf0JpAbTvDuf9DoZcC9Edna5ORERERBymQCf18tGKHWRtLeSxywcSFaaP0SlZPhsWPAxFudA2Cc65z7s6ZcZLkPMlBAVDnwsh/SbofhYEaVsIEREREfHy65u4MWYi8BfABfzNWjvzmPM/B/4HqALygZustVt859zACl/TrdbaixuodnFIeZWbx/6zlr6dYrhieLLT5QS25bPhgzu9C5wAFG2DObd577frCuMegKHXQUyCczWKiIiISLNVa6AzxriAZ4HzgVxgiTFmrrV2dbVmWUC6tbbEGHMb8Dhwle9cqbV2SAPXLQ569escthWU8o+bR+AK0tytenFXwZ718Mmvj4S56iLj4M4f1BsnIiIiIiflTw/dCGCjtTYbwBjzFnAJcDjQWWsXVWv/LTC1IYuU5qPgYAVPL9zI2X06MiZVc7j8UnEQdq2CHT/AzhWwc7l33zh3+YmvKdmrMCciIiIitfIn0CUC26o9zgVOP0n7m4FPqj0ON8Zk4B2OOdNaO6emi4wxtwC3AHTt2tWPssQJsxZs4GB5Ffdd2M/pUpqng3u8gW3Hcu/tzhWwZwNgvefD20HnQTDiJ9B5MMx/AIp3Hv88bZOatGwRERERCUz+BLqaxtTZGhsaMxVIB86qdrirtXa7MaYHsNAYs8Jau+m4J7T2ReBFgPT09BqfX5yVnV/M699u4eoRXemdEON0Oc6yFgq3+ILbiiMh7sD2I23aJkOnQdD/Mm+I6zTIG9SO3WKg+hw6gJAIOPe3TfM+RERERCSg+RPocoHqK18kAduPbWSMOQ+4HzjLWnt4LJm1drvvNtsY8zkwFDgu0Enz98dP1hIWHMQ95/V2upSm5a70buZdPbjtXAHlRd7zJgjiekPKmUeCW6eB/m3yPehK7231VS7P/e2R4yIiIiIiJ+FPoFsCpBpjugN5wNXANdUbGGOGAi8AE621u6sdbw+UWGvLjTFxwGi8C6ZIgPk2ey+frt7FL8f3pmNMmNPlNJ7yYu98t53Lj4S33WuOzHcLjoCE/jDwcm9o6zQYEtK8vWr1NehKBTgRERERqZdaA521tsoYcwcwD++2BS9Za1cZYx4GMqy1c4EngGjgHeMdTnZoe4J+wAvGGA8QhHcO3eoaX0iaLY/Hu4l457bh3HxmC9pEvDj/6OC2czns3cThEcUR7b29baff4g1unQZCbC9wad89EREREWke/Ppmaq39GPj4mGO/rXb/vBNc9zUw8FQKFOfNWZbHyrz9PHXlYCJCXU6XU3fWwr6co4dL7lwOB3YcadO2q3e45MAfeUNc50HQJvH4+W4iIiIiIs2IuhrkpEor3Dwxbx0DE9syZUii0+XUzl0J+Wu9oa36SpPl+73njcs736372CPBLWGAf/PdRERERESaGQU6Oam/L85mR1EZf7pqCEHNbRPx8mLYtbJacDs0363Cez44AjoN8PW6DfSGt/hTnO8mIiIiItKMKNDJCe0+UMZfP9/E+WkJjOwR62wxxbuPnuu2YzkUZHNkvlsHb2A7/X+9+7sdmu8WFIBDREVERERE/KRAJyf0p083UF7l4d4L+jbdi3o8UJhz9HDJHcuP3ny7XVfvcMnBV/tWmhwEbbpovpuIiIiItDoKdFKj9bsO8PaSrVw/KoUeHaPrdvHy2f7tq1ZVcWS+26Fet10rj57v1rEv9Di72v5uA7yrT4qIiIiIiAKd1OwPH68hKiyYO89NrduFy2fDB3dCZan3cdE27+OqMohNPXrYZP7aI/PdQiK9i5MMuvJIr1t8GoSEN+wbExERERFpQRTo5Dhfbsjn83X53HdhXzpEhdbt4gUPHwlzh1SWwtyfHXkcGeftcet5m2+lycHQoYfmu4mIiIiI1JECnRzF7bE8+tEakjtEMO2MlLo/QVHuic/9+G1vkIvprPluIiIiIiINIMjpAqR5eTdzG2t3HuDXE/sSFlyPHrOYhJqPt02GPhO1eImIiIiISANSoJPDDpZX8eT89Qzr2o5JAzvX/QnKi4EawlpIhHdhFBERERERaVAKdHLYC19kk3+gnPsnpWHq2otmLfz7p1C8C0bf4+2Rw3hvJ8+qeZVLERERERE5JZpDJwDsLCrjxS82MWlQZ4Z3q8e2AF/PgtX/hvMfhtF3wfkPNXiNIiIiIiJyNPXQCQBPzl+HxwMzJtZjE/FNi+CzhyDtEjjjzgavTUREREREaqZAJ6zaXsR7S3O5YXQKyR0i63Zx4VZ49yaI6w2XPKsFT0REREREmpACXStnrXebgnYRIdx+Tq+6XVxZCm9PBU8VXPUGhMU0TpEiIiIiIlIjBbpWbuHa3Xy9aS93nZtK24gQ/y+0Fj78Oez4AS59AeLqGAZFREREROSUKdC1YpVuD3/4eA094qK4dmS3ul2c8Xf44Z9w1q+h74WNU6CIiIiIiJyUAl0r9tb3W9mUf5AZF/QlxFWHj8LW7+CTGZA6Hs6a0XgFioiIiIjISSnQtVL7yyr502cbOL17B85PS/D/wgM7Yfb10DYJLnsRgvQREhERERFxivaha6WeW7SJgoMV3D+pn/+biFdVwOxpUL4fpr4HEfXYr05ERERERBqMAl0rtK2ghJe+2sylQxMZlNTO/wvn3w/bvoXL/w6dBjRegSIiIiIi4heNl2uFnpi3DgNMn9DH/4uWvQnfvwij7oCBVzRabSIiIiIi4j8FulZm2bZC5v6wnf8Z050u7SL8u2j7MvjwbkgZA+f9rnELFBERERERvynQtSLeTcRXExcdym1n+7lvXEkBvH0dRMbCFS+DS6N0RURERESaCwW6VmTeqp0sydnHPef3JjrMj2DmccO7N0HxTrjyHxDdsfGLFBERERERv6m7pZWoqPIw85O1pMZHc1V6sn8XLfw9ZC+CybMgaXjjFigiIiIiInWmHrpW4h/fbiFnbwn3TepHsD+biK/+Nyz+Ewy/AYZPa/T6RERERESk7hToWoHCkgpmLdjAmNQ4zu7tx7DJ3Wthzk8hMR0ueLzxCxQRERERkXpRoGsFnl64kf1lldx3oR+biJcVwdvXQkgEXPkaBIc1TZEiIiIiIlJnmkPXwuXsOchr3+Rw5fBk+nVuc/LGHg/86zYo2AzT5kLbxCapUURERERE6keBroV77D9rCXEF8YvxvWtvvPj/YN1HMOGPkHJm4xcnIiIiIiKnREMuW7AlOQV8snInt47tSXyb8JM33vAZLHwUBv4IRt7WNAWKiIiIiMgpUaBroTweyyMfrSGhTRg/Gdv95I0LNsN7N0NCf+8WBbXNsxMRERERkWbBr0BnjJlojFlnjNlojJlRw/mfG2NWG2OWG2MWGGO6VTs3zRizwfej9e+byAfLt/PDtkJ+Ob4PkaEnGVlbUQJvTwUsXPU6hEY2WY0iIiIiInJqag10xhgX8CxwAZAG/NgYk3ZMsywg3Vo7CHgXeNx3bQfgQeB0YATwoDGmfcOVLzUpq3Tz+H/Wkda5DZcPSzpxQ2vhgzth1yq4/CXoUEtPnoiIiIiINCv+9NCNADZaa7OttRXAW8Al1RtYaxdZa0t8D78FDqWICcCn1toCa+0+4FNgYsOULify8lc55BWW8ptJ/QgKOsnwye+ehxXvwLj7IfW8pitQREREREQahD+BLhHYVu1xru/YidwMfFLXa40xtxhjMowxGfn5+X6UJTXZW1zOc4s2cm7feM7oFXfihjmLYd790GcSnPmLpitQREREREQajD+BrqYuHltjQ2OmAunAE3W91lr7orU23Vqb3rFjRz/Kkpr8+bMNlFS6uffCfiduVJQH79zgHWJ56V8hSGvjiIiIiIgEIn++yecCydUeJwHbj21kjDkPuB+42FpbXpdrpWFs3H2Af36/lWtGdKVXfHTNjarKYfb1UFkKV70B4W2btkgREREREWkw/gS6JUCqMaa7MSYUuBqYW72BMWYo8ALeMLe72ql5wHhjTHvfYijjfcekEfzx47VEhri4+7zUEzf65FeQlwFTnoP4vk1XnIiIiIiINLiTrGfvZa2tMsbcgTeIuYCXrLWrjDEPAxnW2rl4h1hGA+8Y7x5mW621F1trC4wxv8cbCgEettYWNMo7aeW+3riHBWt38+uJfYmNDqu5UearkPkKnHkPpF1ScxsREREREQkYxtoap7Q5Kj093WZkZDhdRsBweyyTn15MUWklC35xFuEhruMb5WbCyxOh2xkw9X0IqqGNiIiIiIg0C8aYTGttem3ttBpGC/CvrDxW79jPryb2qTnMFefD7OsguhNc8bLCnIiIiIhIC1HrkEtp3kor3Dw5bx2Dk9oyeVCX4xu4q+DdG6FkL9w0DyI7NH2RIiIiIiLSKNRDF+D+35fZ7Nxfxm8uSqt5E/HPHoScL+GiP0OXIU1foIiIiIiINBoFugC2e38Zz/93ExP7d+K0lBp63la8C988AyNugSE/bvoCRURERESkUSnQBbCnPl1PpdvDjAtq2H5g50qY+zNIHgnjH2364kREREREpNEp0AWotTv3MztjG9eNTCElLurok6X74O2pENYGrnwVgkOdKVJERERERBqVFkUJUI9+tIaY8BDuPLfX0Sc8Hnj/FijKhRs+gphOzhQoIiIiIiKNTj10Aejzdbv5csMefjauF+0ij+l9++9M2DAfJv4Rup7uTIEiIiIiItIkFOgCTJXbwx8+XkO32EiuH5Vy9Ml1n8B/H4Mh18Jp/+NIfSIiIiIi0nQU6ALM7Ixc1u8qZsbEvoQGV/vPt2ejd6hl58Ew6f/A1LCFgYiIiIiItCgKdAGkuLyKpz5dx2kp7Zk4oNrcuPJiePtaCAqGq16HkAjnihQRERERkSajQBdAnv98E3uKK7h/UhrmUA+ctfDv22HPerjiJWjX1dkiRURERESkySjQBYjthaX8vy+zuXhwF4Yktzty4utZsHoOnPsg9DzHuQJFRERERKTJKdAFiCfnrcMCv5rY58jB7M/hs4cg7RIYfZdDlYmIiIiIiFMU6ALAitwi3s/K46bR3UlqH+k9WLgV3rkR4nrDJc9qERQRERERkVZIga6Zs9byyEer6RAVyk/P6ek9WFkKb08FTxVc9QaExThbpIiIiIiIOEKBrpn7dPUuvttcwD3npdImPMS7CMpHv4AdP8ClL0BcL6dLFBERERERhyjQNWOVbg8zP1lLz45R/HiEb/XKjL/Dsjdg7K+g74XOFigiIiIiIo5SoGvG3vh2C9l7DnLfhf0IdgXB1u/gkxmQOh7Ovtfp8kRERERExGEKdM1UUWklf1mwgTN6xjKubzwc2Amzr4e2SXDZixCk/3QiIiIiIq1dsNMFSM2eW7SRwtJK7p/UD+OuhNnToHw/TH0PIto7XZ6IiIiIiDQDCnTN0LaCEl7+KofLhyXRv0tb+Hg6bPsWLv87dBrgdHkiIiIiItJMaNxeM/TYf9YSFAS/HN8Hlr0J378II2+HgVc4XZqIiIiIiDQjCnTNzNKt+/hw+Q5uGdODTiXr4MO7IWUMnP+w06WJiIiIiEgzoyGXzYi1lkc+XE3HmDBuPa09vHouRMbCFS+DS/+pRERERETkaOqha0Y+XrGTpVsL+eV5PYn64BbvypZX/gOiOzpdmoiIiIiINEPq9mkmyqvczPzPGvp2iuFH+1+F7EUweRYkDXe6NBERERERaaYU6JqJ177ewraCUj4+r4CgxX+CYdNg+DSnyxIRERERkWZMQy6bgX0HK3h64Qau7l5C2vczIHE4XPiE02WJiIiIiEgzp0DXDPxlwQYo38/DpX+EkAjvvLngMKfLEhERERGRZk5DLh2WnV/MG99uZk7HVwktyoFpc6FtotNliYiIiIhIAFCgc9jMT9ZyR/Bc+u//Eib8EVLOdLokEREREREJEBpy6aBvs/dSvnY+dwbNhoE/gpG3OV2SiIiIiIgEEAU6h3g8lr/NXcjToc9i49Ng8l/AGKfLEhERERGRAOJXoDPGTDTGrDPGbDTGzKjh/FhjzFJjTJUx5opjzrmNMct8P3MbqvBA92HmRn5e8HvCQ4IIuvp1CI1yuiQREREREQkwtc6hM8a4gGeB84FcYIkxZq61dnW1ZluBG4Bf1vAUpdbaIQ1Qa4tRVlFF2Cf30DdoK1wxGzr0cLokEREREREJQP700I0ANlprs621FcBbwCXVG1hrc6y1ywFPI9TY4ix5+w9M8HxJ3pB7COoz3ulyREREREQkQPkT6BKBbdUe5/qO+SvcGJNhjPnWGDPlRI2MMbf42mXk5+fX4ekDS+HqRYza+CeyIs8g+eIHnC5HREREREQCmD+BrqaVOmwdXqOrtTYduAb4szGmZ02NrLUvWmvTrbXpHTt2rMPTB5D92wl+/0a2kkC7a/4OQVqTRkRERERE6s+fRJELJFd7nARs9/cFrLXbfbfZwOfA0DrU13JUlVP6+jVQWconaU/SPamL0xWJiIiIiEiA8yfQLQFSjTHdjTGhwNWAX6tVGmPaG2PCfPfjgNHA6pNf1UJ98msidmfxgPkpP75I8+ZEREREROTU1RrorLVVwB3APGANMNtau8oY87Ax5mIAY8xpxphc4EfAC8aYVb7L+wEZxpgfgEXAzGNWx2wdlr4GmS/z16rJ9Bs3lQ5RoU5XJCIiIiIiLUCt2xYAWGs/Bj4+5thvq91fgnco5rHXfQ0MPMUaA1tuJvajX5AVPIS3IqYx/4wUpysSEREREZEWQqtyNKbifJh9HSWhcdxc/L9Mv6A/YcEup6sSEREREZEWQoGusbir4N0bsSV7+d/Ke0jp2pVJAzs7XZWIiIiIiLQgCnSN5bMHIedL5nf/NV8WJ/KbSf0wpqYdIEREREREROpHga4xrHgXvnmGksE3cffaNCYN7Mzwbh2crkpERERERFoYBbqGtmsVzP0ZJI/k4YprcXssv57Y1+mqRERERESkBVKga0il++CtayGsDevOepq3l+1i2hnd6Bob6XRlIiIiIiLSAvm1bYH4weOB92+BolzsDR/y8PwC2kaEcMc5qU5XJiIiIiIiLZR66BrKfx+DDfNh4h9ZVNKdrzbu5a5zU2kbGeJ0ZSIiIiIi0kIp0DWEdZ/Af2fC4GuoGnYTf/h4Ld3jorj29G5OVyYiIiIiIi2YAt2p2rvJO9Sy82C46CnezMhl4+5iZlzQl9Bg/fGKiIiIiEjjUeI4FeXF8NY1EBQMV73OAXcwf/50PSO6d2B8WoLT1YmIiIiISAunRVHqy1r49+2wZz1MfR/adeW5/6xl78EKXtYm4iIiIiIi0gTUQ1dfXz8Nq+fAuQ9Cz3PI3VfC3xdv5tKhiQxKaud0dSIiIiIi0goo0NVH9ufw2YOQdgmMvguAJ+etwwDTJ/RxtDQREREREWk9FOjqqnArvHMjxPWGS54FY/hhWyFzlm3nf8Z0p0u7CKcrFBERERGRVkKBri4qS+Ht68BTBVe9AWExWGt59KM1xEWHctvZvZyuUEREREREWhEtiuKP5bNhwcNQtM37eOTtEOcNb/NW7eL7nAIevXQA0WH64xSRxldZWUlubi5lZWVOlyJSL+Hh4SQlJRESEuJ0KSIiAU8JpDbLZ8MHd3p75w7JfAm6DKEi7QpmfrKG1PhorkpPdq5GEWlVcnNziYmJISUlRSvqSsCx1rJ3715yc3Pp3r270+WIiAQ8DbmszYKHjw5z4H284GFe/3YLOXtLuG9SP4Jd+qMUkaZRVlZGbGyswpwEJGMMsbGx6mEWEWkgSiG1Kcqt8bAtymXWwg2MSY3j7N4dm7goEWntFOYkkOnzKyLScBToatM2qcbDRaEJFJVWct+F2kRcREREREScoUBXm3N/CyFHb0XgCY7gdyWXc+XwZPp1buNQYSIi/pmTlcfomQvpPuMjRs9cyJysPKdLat2Wz4Y/DYCH2nlvl88+5afMyclhwIABDVDc8T7//HMuuugiAObOncvMmTMb5XVERKR+FOhqM+hKmDwL2iYDBtom80rsPcwLGssvxvd2ujoRkZOak5XHve+vIK+wFAvkFZZy7/srGjXUXXjhhRQWFlJYWMhzzz13+Hj1YNBqHVpoq2gbYL23H9zZIKGuKVx88cXMmDHD6TJERKQarXLpj0FXen+AjJwCHn7+G+45ryfxbcIdLkxEWrvffbCK1dv3n/B81tZCKtyeo46VVrr51bvLefP7rTVek9alDQ9O7l/vmj7++GPA22v03HPP8dOf/rTez1VfVVVVBAc78E/cJzNg54oTn89dAu7yo49VlsK/74DMV2u+ptNAuKD2XrGqqiqmTZtGVlYWvXv35rXXXuPJJ5/kgw8+oLS0lDPOOIMXXngBYwyzZs3i+eefJzg4mLS0NN566y0OHjzIz372M1asWEFVVRUPPfQQl1xyyVGv8corr5CRkcEzzzzDDTfcQJs2bcjIyGDnzp08/vjjXHHFFQA88cQTzJ49m/Lyci699FJ+97vf1Vq/iIjUj3ro6sBayyMfrSGhTRg/GaullkWk+Ts2zNV23B+PP/44s2bNAuCee+5h3LhxACxYsICpU6eSkpLCnj17mDFjBps2bWLIkCFMnz4dgOLiYq644gr69u3Ltddei7X2hK+TkpLCgw8+yLBhwxg4cCBr164FoKCggClTpjBo0CBGjhzJ8uXLAXjooYe45ZZbGD9+PNdffz2vvPIKU6ZMYfLkyXTv3p1nnnmGp556iqFDhzJy5EgKCgrq/WdQb8eGudqO18G6deu45ZZbWL58OW3atOG5557jjjvuYMmSJaxcuZLS0lI+/PBDAGbOnElWVhbLly/n+eefB+DRRx9l3LhxLFmyhEWLFjF9+nQOHjx40tfcsWMHixcv5sMPPzzcczd//nw2bNjA999/z7Jly8jMzOSLL7445fcnIiI1Uw9dHXywfAfLthXy+BWDiAzVH52IOK+2nrTRMxeSV1h63PHEdhG8feuoer3m2LFj+b//+z/uvPNOMjIyKC8vp7KyksWLFzNmzBgWL14MeEPDypUrWbZsGeAdcpmVlcWqVavo0qULo0eP5quvvuLMM8884WvFxcWxdOlSnnvuOZ588kn+9re/8eCDDzJ06FDmzJnDwoULuf766w+/RmZmJosXLyYiIoJXXnmFlStXkpWVRVlZGb169eKxxx4jKyuLe+65h9dee4277767Xn8GJ1RbT9qfBviGWx6jbTLc+NEpvXRycjKjR48GYOrUqcyaNYvu3bvz+OOPU1JSQkFBAf3792fy5Oimy70AAAteSURBVMkMGjSIa6+9lilTpjBlyhTAG8Tmzp3Lk08+CXi3x9i6teZe3EOmTJlCUFAQaWlp7Nq16/DzzJ8/n6FDhwLeEL9hwwbGjh17Su9PRERqph46P8zJyuOMPy7gzjezCA4yBGtVSxEJENMn9CEixHXUsYgQF9Mn9Kn3cw4fPpzMzEwOHDhAWFgYo0aNIiMjgy+//JIxY8ac9NoRI0aQlJREUFAQQ4YMIScn56TtL7vsssOveajt4sWLue666wAYN24ce/fupaioCPDO8YqIOLKQ1TnnnENMTAwdO3akbdu2TJ48GYCBAwfW+tqNooaFtgiJ8B4/RceuuGyM4ac//SnvvvsuK1as4Cc/+cnhvd8++ugjbr/9djIzMxk+fDhVVVVYa3nvvfdYtmwZy5YtY+vWrfTr1++krxkWFnb4/qHeVmst99577+Hn2bhxIzfffPMpvz8REamZAl0tDi0osL3I+49glcdy/5yVWiVORALClKGJ/PGygSS2i8Dg7Zn742UDmTI0sd7PGRISQkpKCi+//DJnnHEGY8aMYdGiRWzatKlOAcDlclFVVeVX++ptaxqmeSjMREVFnfD1goKCDj8OCgqq9bUbRQ0LbTF51uF52qdi69atfPPNNwC8+eabh3s+4+LiKC4u5t133wXA4/Gwbds2zjnnHB5//HEKCwspLi5mwoQJPP3004f/fLOysupVx4QJE3jppZcoLi4GIC8vj927d5/q2xMRkRPQuMFaPDFvHaWV7qOOlVa6eWLeulP6QiQi0lSmDE1s8L+vxo4dy5NPPslLL73EwIED+fnPf87w4cOP6iWKiYnhwIEDDfq6h177jTfe4IEHHuDzzz8nLi6ONm0CaAuZagttNaR+/frx6quvcuutt5Kamsptt93Gvn37GDhwICkpKZx22mkAuN1upk6dSlFREdZa7rnnHtq1a8cDDzzA3XffzaBBg7DWkpKScnjOXV2MHz+eNWvWMGqUd0hvdHQ0r7/+OvHx8Q36fkVExEuBrhbba5h7crLjIiKtwZgxY3j00UcZNWoUUVFRhIeHHzfcMjY2ltGjRzNgwAAuuOACJk2a1CCv/dBDD3HjjTcyaNAgIiMjefXVE6wO2YqkpKSwevXq444/8sgjPPLII8cdPzTPsbqIiAheeOGF446fffbZnH322QDccMMN3HDDDYB3xcvqDvXIAdx1113cdddddXgHIiJSX+ZkK4w5JT093WZkZDhdBnDyBQW+mjHOgYpEpLVbs2ZNrUMbRZo7fY5FRE7OGJNprU2vrZ3m0NWiMRYUEBERERERaQgaclmLQ/NOnpi3ju2FpXRpF8H0CX00f05EpIFceumlbN68+ahjjz32GBMmTHCoIhERkcDhV6AzxkwE/gK4gL9Za2cec34s8GdgEHC1tfbdauemAb/xPXzEWhtwkx0aY0EBEZFTYa09bpn6QPWvf/3L6RKkiTXH6R4iIoGq1iGXxhgX8CxwAZAG/NgYk3ZMs63ADcA/j7m2A/AgcDowAnjQGNP+1MsWEWm9wsPD2bt3r74US0Cy1rJ3717Cw8OdLkVEpEXwp4duBLDRWpsNYIx5C7gEOLyclrU2x3fOc8y1E4BPrbUFvvOfAhOBN0+5chGRViopKYnc3Fzy8/OdLkWkXsLDw0lKSnK6DBGRFsGfQJcIbKv2OBdvj5s/arpWYxdFRE5BSEgI3bt3d7oMERERaQb8WeWypkka/o7z8ftaY8wtxpgMY0yGfussIiIiIiJSO38CXS6QXO1xErDdz+f3+1pr7YvW2nRrbXrHjh39fHoREREREZHWy59AtwRINcZ0N8aEAlcDc/18/nnAeGNMe99iKON9x0REREREROQUGX9WSTPGXIh3WwIX8JK19lFjzMNAhrV2rjHmNOBfQHugDNhpre3vu/Ym4D7fUz1qrX3Zj9fLB7bU5w01sjhgj9NFSIulz5c0Jn2+pDHp8yWNSZ8vaWzN9TPWzVpb69BFvwKdeBljMqy16U7XIS2TPl/SmPT5ksakz5c0Jn2+pLEF+mfMnyGXIiIiIiIi0gwp0ImIiIiIiAQoBbq6edHpAqRF0+dLGpM+X9KY9PmSxqTPlzS2gP6MaQ6diIiIiIhIgFIPnYiIiIiISIBSoBMREREREQlQCnR+MMZMNMasM8ZsNMbMcLoeaTmMMcnGmEXGmDXGmFXGmLucrklaHmOMyxiTZYz50OlapOUxxrQzxrxrjFnr+7tslNM1ScthjLnH9+/jSmPMm8aYcKdrksBljHnJGLPbGLOy2rEOxphPjTEbfLftnayxPhToamGMcQHPAhcAacCPjTFpzlYlLUgV8AtrbT9gJHC7Pl/SCO4C1jhdhLRYfwH+Y63tCwxGnzVpIMaYROBOIN1aOwBwAVc7W5UEuFeAicccmwEssNamAgt8jwOKAl3tRgAbrbXZ1toK4C3gEodrkhbCWrvDWrvUd/8A3i9Cic5WJS2JMSYJmAT8zelapOUxxrQBxgJ/B7DWVlhrC52tSlqYYCDCGBMMRALbHa5HApi19gug4JjDlwCv+u6/Ckxp0qIagAJd7RKBbdUe56Iv3NIIjDEpwFDgO2crkRbmz8CvAI/ThUiL1APIB172Dev9mzEmyumipGWw1uYBTwJbgR1AkbV2vrNVSQuUYK3dAd5ftAPxDtdTZwp0tTM1HNNeD9KgjDHRwHvA3dba/U7XIy2DMeYiYLe1NtPpWqTFCgaGAX+11g4FDhKAw5WkefLNZboE6A50AaKMMVOdrUqk+VGgq10ukFztcRLq7pcGZIwJwRvm3rDWvu90PdKijAYuNsbk4B0uPs4Y87qzJUkLkwvkWmsPjSx4F2/AE2kI5wGbrbX51tpK4H3gDIdrkpZnlzGmM4DvdrfD9dSZAl3tlgCpxpjuxphQvJNx5zpck7QQxhiDd+7JGmvtU07XIy2LtfZea22StTYF799dC621+u22NBhr7U5gmzGmj+/QucBqB0uSlmUrMNIYE+n79/JctOiONLy5wDTf/WnAvx2spV6CnS6gubPWVhlj7gDm4V1d6SVr7SqHy5KWYzRwHbDCGLPMd+w+a+3HDtYkIlIXPwPe8P3SMxu40eF6pIWw1n5njHkXWIp3Vegs4EVnq5JAZox5EzgbiDPG5AIPAjOB2caYm/H+EuFHzlVYP8ZaTQcTEREREREJRBpyKSIiIiIiEqAU6ERERERERAKUAp2IiIiIiEiAUqATEREREREJUAp0IiIiIiIiAUqBTkREWixjjNsYs6zaz4wGfO4UY8zKhno+ERGR+tA+dCIi0pKVWmuHOF2EiIhIY1EPnYiItDrGmBxjzGPGmO99P718x7sZYxYYY5b7brv6jicYY/5ljPnB93OG76lcxpj/Z4xZZYyZb4yJcOxNiYhIq6RAJyIiLVnEMUMur6p2br+1dgTwDPBn37FngNestYOAN4BZvuOzgP9aawcDw4BVvuOpwLPW2v5AIXB5I78fERGRoxhrrdM1iIiINApjTLG1NrqG4znAOGtttjEmBNhprY01xuwBOltrK33Hd1hr44wx+UCStba82nOkAJ9aa1N9j38NhFhrH2n8dyYiIuKlHjoREWmt7Anun6hNTcqr3XejuekiItLEFOhERKS1uqra7Te++18DV/vuXwss9t1fANwGYIxxGWPaNFWRIiIiJ6PfJIqISEsWYYxZVu3xf6y1h7YuCDPGfIf3l5s/9h27E3jJGDMdyAdu9B2/C3jRGHMz3p6424AdjV69iIhILTSHTkREWh3fHLp0a+0ep2sRERE5FRpyKSIiIiIiEqDUQyciIiIiIhKg1EMnIiIiIiISoBToREREREREApQCnYiIiIiISIBSoBMREREREQlQCnQiIiIiIiIB6v8DnyQ21O2v8yEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_training_history(title, label, baseline, bn_solvers, plot_fn, bl_marker='.', bn_marker='.', labels=None):\n",
    "    \"\"\"utility function for plotting training history\"\"\"\n",
    "    plt.title(title)\n",
    "    plt.xlabel(label)\n",
    "    bn_plots = [plot_fn(bn_solver) for bn_solver in bn_solvers]\n",
    "    bl_plot = plot_fn(baseline)\n",
    "    num_bn = len(bn_plots)\n",
    "    for i in range(num_bn):\n",
    "        label='with_norm'\n",
    "        if labels is not None:\n",
    "            label += str(labels[i])\n",
    "        plt.plot(bn_plots[i], bn_marker, label=label)\n",
    "    label='baseline'\n",
    "    if labels is not None:\n",
    "        label += str(labels[0])\n",
    "    plt.plot(bl_plot, bl_marker, label=label)\n",
    "    plt.legend(loc='lower center', ncol=num_bn+1) \n",
    "\n",
    "    \n",
    "plt.subplot(3, 1, 1)\n",
    "plot_training_history('Training loss','Iteration', solver, [bn_solver], \\\n",
    "                      lambda x: x.loss_history, bl_marker='o', bn_marker='o')\n",
    "plt.subplot(3, 1, 2)\n",
    "plot_training_history('Training accuracy','Epoch', solver, [bn_solver], \\\n",
    "                      lambda x: x.train_acc_history, bl_marker='-o', bn_marker='-o')\n",
    "plt.subplot(3, 1, 3)\n",
    "plot_training_history('Validation accuracy','Epoch', solver, [bn_solver], \\\n",
    "                      lambda x: x.val_acc_history, bl_marker='-o', bn_marker='-o')\n",
    "\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Batch normalization and initialization\n",
    "We will now run a small experiment to study the interaction of batch normalization and weight initialization.\n",
    "\n",
    "The first cell will train 8-layer networks both with and without batch normalization using different scales for weight initialization. The second layer will plot training accuracy, validation set accuracy, and training loss as a function of the weight initialization scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running weight scale 1 / 20\n",
      "Running weight scale 2 / 20\n",
      "Running weight scale 3 / 20\n",
      "Running weight scale 4 / 20\n",
      "Running weight scale 5 / 20\n",
      "Running weight scale 6 / 20\n",
      "Running weight scale 7 / 20\n",
      "Running weight scale 8 / 20\n",
      "Running weight scale 9 / 20\n",
      "Running weight scale 10 / 20\n",
      "Running weight scale 11 / 20\n",
      "Running weight scale 12 / 20\n",
      "Running weight scale 13 / 20\n",
      "Running weight scale 14 / 20\n",
      "Running weight scale 15 / 20\n",
      "Running weight scale 16 / 20\n",
      "Running weight scale 17 / 20\n",
      "Running weight scale 18 / 20\n",
      "Running weight scale 19 / 20\n",
      "Running weight scale 20 / 20\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "# Try training a very deep net with batchnorm\n",
    "hidden_dims = [50, 50, 50, 50, 50, 50, 50]\n",
    "num_train = 1000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "bn_solvers_ws = {}\n",
    "solvers_ws = {}\n",
    "weight_scales = np.logspace(-4, 0, num=20)\n",
    "for i, weight_scale in enumerate(weight_scales):\n",
    "    print('Running weight scale %d / %d' % (i + 1, len(weight_scales)))\n",
    "    bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization='batchnorm')\n",
    "    model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n",
    "\n",
    "    bn_solver = Solver(bn_model, small_data,\n",
    "                  num_epochs=10, batch_size=50,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  verbose=False, print_every=200)\n",
    "    bn_solver.train()\n",
    "    bn_solvers_ws[weight_scale] = bn_solver\n",
    "\n",
    "    solver = Solver(model, small_data,\n",
    "                  num_epochs=10, batch_size=50,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  verbose=False, print_every=200)\n",
    "    solver.train()\n",
    "    solvers_ws[weight_scale] = solver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot results of weight scale experiment\n",
    "best_train_accs, bn_best_train_accs = [], []\n",
    "best_val_accs, bn_best_val_accs = [], []\n",
    "final_train_loss, bn_final_train_loss = [], []\n",
    "\n",
    "for ws in weight_scales:\n",
    "    best_train_accs.append(max(solvers_ws[ws].train_acc_history))\n",
    "    bn_best_train_accs.append(max(bn_solvers_ws[ws].train_acc_history))\n",
    "  \n",
    "    best_val_accs.append(max(solvers_ws[ws].val_acc_history))\n",
    "    bn_best_val_accs.append(max(bn_solvers_ws[ws].val_acc_history))\n",
    "  \n",
    "    final_train_loss.append(np.mean(solvers_ws[ws].loss_history[-100:]))\n",
    "    bn_final_train_loss.append(np.mean(bn_solvers_ws[ws].loss_history[-100:]))\n",
    "    \n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Best val accuracy vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Best val accuracy')\n",
    "plt.semilogx(weight_scales, best_val_accs, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_best_val_accs, '-o', label='batchnorm')\n",
    "plt.legend(ncol=2, loc='lower right')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Best train accuracy vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Best training accuracy')\n",
    "plt.semilogx(weight_scales, best_train_accs, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_best_train_accs, '-o', label='batchnorm')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Final training loss vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Final training loss')\n",
    "plt.semilogx(weight_scales, final_train_loss, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_final_train_loss, '-o', label='batchnorm')\n",
    "plt.legend()\n",
    "plt.gca().set_ylim(1.0, 3.5)\n",
    "\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## Inline Question 1:\n",
    "Describe the results of this experiment. How does the scale of weight initialization affect models with/without batch normalization differently, and why?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## Answer:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batch normalization and batch size\n",
    "We will now run a small experiment to study the interaction of batch normalization and batch size.\n",
    "\n",
    "The first cell will train 6-layer networks both with and without batch normalization using different batch sizes. The second layer will plot training accuracy and validation set accuracy over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No normalization: batch size =  5\n",
      "Normalization: batch size =  5\n",
      "Normalization: batch size =  10\n",
      "Normalization: batch size =  50\n"
     ]
    }
   ],
   "source": [
    "def run_batchsize_experiments(normalization_mode):\n",
    "    np.random.seed(231)\n",
    "    # Try training a very deep net with batchnorm\n",
    "    hidden_dims = [100, 100, 100, 100, 100]\n",
    "    num_train = 1000\n",
    "    small_data = {\n",
    "      'X_train': data['X_train'][:num_train],\n",
    "      'y_train': data['y_train'][:num_train],\n",
    "      'X_val': data['X_val'],\n",
    "      'y_val': data['y_val'],\n",
    "    }\n",
    "    n_epochs=10\n",
    "    weight_scale = 2e-2\n",
    "    batch_sizes = [5,10,50]\n",
    "    lr = 10**(-3.5)\n",
    "    solver_bsize = batch_sizes[0]\n",
    "\n",
    "    print('No normalization: batch size = ',solver_bsize)\n",
    "    model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n",
    "    solver = Solver(model, small_data,\n",
    "                    num_epochs=n_epochs, batch_size=solver_bsize,\n",
    "                    update_rule='adam',\n",
    "                    optim_config={\n",
    "                      'learning_rate': lr,\n",
    "                    },\n",
    "                    verbose=False)\n",
    "    solver.train()\n",
    "    \n",
    "    bn_solvers = []\n",
    "    for i in range(len(batch_sizes)):\n",
    "        b_size=batch_sizes[i]\n",
    "        print('Normalization: batch size = ',b_size)\n",
    "        bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=normalization_mode)\n",
    "        bn_solver = Solver(bn_model, small_data,\n",
    "                        num_epochs=n_epochs, batch_size=b_size,\n",
    "                        update_rule='adam',\n",
    "                        optim_config={\n",
    "                          'learning_rate': lr,\n",
    "                        },\n",
    "                        verbose=False)\n",
    "        bn_solver.train()\n",
    "        bn_solvers.append(bn_solver)\n",
    "        \n",
    "    return bn_solvers, solver, batch_sizes\n",
    "\n",
    "batch_sizes = [5,10,50]\n",
    "bn_solvers_bsize, solver_bsize, batch_sizes = run_batchsize_experiments('batchnorm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2, 1, 1)\n",
    "plot_training_history('Training accuracy (Batch Normalization)','Epoch', solver_bsize, bn_solvers_bsize, \\\n",
    "                      lambda x: x.train_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n",
    "plt.subplot(2, 1, 2)\n",
    "plot_training_history('Validation accuracy (Batch Normalization)','Epoch', solver_bsize, bn_solvers_bsize, \\\n",
    "                      lambda x: x.val_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n",
    "\n",
    "plt.gcf().set_size_inches(15, 10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inline Question 2:\n",
    "Describe the results of this experiment. What does this imply about the relationship between batch normalization and batch size? Why is this relationship observed?\n",
    "\n",
    "## Answer:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Layer Normalization\n",
    "Batch normalization has proved to be effective in making networks easier to train, but the dependency on batch size makes it less useful in complex networks which have a cap on the input batch size due to hardware limitations. \n",
    "\n",
    "Several alternatives to batch normalization have been proposed to mitigate this problem; one such technique is Layer Normalization [4]. Instead of normalizing over the batch, we normalize over the features. In other words, when using Layer Normalization, each feature vector corresponding to a single datapoint is normalized based on the sum of all terms within that feature vector.\n",
    "\n",
    "[4] [Ba, Jimmy Lei, Jamie Ryan Kiros, and Geoffrey E. Hinton. \"Layer Normalization.\" stat 1050 (2016): 21.](https://arxiv.org/pdf/1607.06450.pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inline Question 3:\n",
    "Which of these data preprocessing steps is analogous to batch normalization, and which is analogous to layer normalization?\n",
    "\n",
    "1. Scaling each image in the dataset, so that the RGB channels for each row of pixels within an image sums up to 1.\n",
    "2. Scaling each image in the dataset, so that the RGB channels for all pixels within an image sums up to 1.  \n",
    "3. Subtracting the mean image of the dataset from each image in the dataset.\n",
    "4. Setting all RGB values to either 0 or 1 depending on a given threshold.\n",
    "\n",
    "## Answer:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Layer Normalization: Implementation\n",
    "\n",
    "Now you'll implement layer normalization. This step should be relatively straightforward, as conceptually the implementation is almost identical to that of batch normalization. One significant difference though is that for layer normalization, we do not keep track of the moving moments, and the testing phase is identical to the training phase, where the mean and variance are directly calculated per datapoint.\n",
    "\n",
    "Here's what you need to do:\n",
    "\n",
    "* In `cs231n/layers.py`, implement the forward pass for layer normalization in the function `layernorm_backward`. \n",
    "\n",
    "Run the cell below to check your results.\n",
    "* In `cs231n/layers.py`, implement the backward pass for layer normalization in the function `layernorm_backward`. \n",
    "\n",
    "Run the second cell below to check your results.\n",
    "* Modify `cs231n/classifiers/fc_net.py` to add layer normalization to the `FullyConnectedNet`. When the `normalization` flag is set to `\"layernorm\"` in the constructor, you should insert a layer normalization layer before each ReLU nonlinearity. \n",
    "\n",
    "Run the third cell below to run the batch size experiment on layer normalization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before layer normalization:\n",
      "  means:  [-59.06673243 -47.60782686 -43.31137368 -26.40991744]\n",
      "  stds:   [10.07429373 28.39478981 35.28360729  4.01831507]\n",
      "\n",
      "After layer normalization (gamma=1, beta=0)\n",
      "  means:  [-4.81096644e-16  0.00000000e+00  7.40148683e-17 -5.55111512e-16]\n",
      "  stds:   [0.99999995 0.99999999 1.         0.99999969]\n",
      "\n",
      "After layer normalization (gamma= [3. 3. 3.] , beta= [5. 5. 5.] )\n",
      "  means:  [5. 5. 5. 5.]\n",
      "  stds:   [2.99999985 2.99999998 2.99999999 2.99999907]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after layer normalization   \n",
    "\n",
    "# Simulate the forward pass for a two-layer network\n",
    "np.random.seed(231)\n",
    "N, D1, D2, D3 =4, 50, 60, 3\n",
    "X = np.random.randn(N, D1)\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "\n",
    "print('Before layer normalization:')\n",
    "print_mean_std(a,axis=1)\n",
    "\n",
    "gamma = np.ones(D3)\n",
    "beta = np.zeros(D3)\n",
    "# Means should be close to zero and stds close to one\n",
    "print('After layer normalization (gamma=1, beta=0)')\n",
    "a_norm, _ = layernorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print_mean_std(a_norm,axis=1)\n",
    "\n",
    "gamma = np.asarray([3.0,3.0,3.0])\n",
    "beta = np.asarray([5.0,5.0,5.0])\n",
    "# Now means should be close to beta and stds close to gamma\n",
    "print('After layer normalization (gamma=', gamma, ', beta=', beta, ')')\n",
    "a_norm, _ = layernorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print_mean_std(a_norm,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx error:  1.3050040640816791e-09\n",
      "dgamma error:  4.759518365681667e-12\n",
      "dbeta error:  2.6853898000928757e-12\n"
     ]
    }
   ],
   "source": [
    "# Gradient check batchnorm backward pass\n",
    "np.random.seed(231)\n",
    "N, D = 4, 5\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "ln_param = {}\n",
    "fx = lambda x: layernorm_forward(x, gamma, beta, ln_param)[0]\n",
    "fg = lambda a: layernorm_forward(x, a, beta, ln_param)[0]\n",
    "fb = lambda b: layernorm_forward(x, gamma, b, ln_param)[0]\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma.copy(), dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta.copy(), dout)\n",
    "\n",
    "_, cache = layernorm_forward(x, gamma, beta, ln_param)\n",
    "dx, dgamma, dbeta = layernorm_backward(dout, cache)\n",
    "\n",
    "#You should expect to see relative errors between 1e-12 and 1e-8\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dgamma error: ', rel_error(da_num, dgamma))\n",
    "print('dbeta error: ', rel_error(db_num, dbeta))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Layer Normalization and batch size\n",
    "\n",
    "We will now run the previous batch size experiment with layer normalization instead of batch normalization. Compared to the previous experiment, you should see a markedly smaller influence of batch size on the training history!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No normalization: batch size =  5\n",
      "Normalization: batch size =  5\n",
      "Normalization: batch size =  10\n",
      "Normalization: batch size =  50\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ln_solvers_bsize, solver_bsize, batch_sizes = run_batchsize_experiments('layernorm')\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "plot_training_history('Training accuracy (Layer Normalization)','Epoch', solver_bsize, ln_solvers_bsize, \\\n",
    "                      lambda x: x.train_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n",
    "plt.subplot(2, 1, 2)\n",
    "plot_training_history('Validation accuracy (Layer Normalization)','Epoch', solver_bsize, ln_solvers_bsize, \\\n",
    "                      lambda x: x.val_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n",
    "\n",
    "plt.gcf().set_size_inches(15, 10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inline Question 4:\n",
    "When is layer normalization likely to not work well, and why?\n",
    "\n",
    "1. Using it in a very deep network\n",
    "2. Having a very small dimension of features\n",
    "3. Having a high regularization term\n",
    "\n",
    "\n",
    "## Answer:\n",
    "\n"
   ]
  }
 ],
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   "language": "python",
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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   "title_sidebar": "Contents",
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   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
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